Journal articles on the topic 'Image biomarker reproducibility'
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Ram, Sripad, Pamela Vizcarra, Pamela Whalen, Shibing Deng, C. L. Painter, Amy Jackson-Fisher, Steven Pirie-Shepherd, Xiaoling Xia, and Eric L. Powell. "Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images." PLOS ONE 16, no. 9 (September 27, 2021): e0245638. http://dx.doi.org/10.1371/journal.pone.0245638.
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Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.
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Monti, Caterina Beatrice, Marco Alì, Davide Capra, Federico Wiedenmann, Giulia Lastella, Francesco Secchi, and Francesco Sardanelli. "Ultrasound semiautomatic versus manual estimation of carotid intima-media thickness: reproducibility and cardiovascular risk stratification." Medical Ultrasonography 22, no. 4 (November 18, 2020): 402. http://dx.doi.org/10.11152/mu-2416.
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Aims: Carotid intima-media thickness (CIMT) is used increasingly as an imaging biomarker of cardiovascular risk (CVR). Our aim was to compare semiautomatic CIMT (sCIMT) versus manual CIMT (mCIMT) for reproducibility and prediction of CVR.Materials and methods: Two independent readers measured sCIMT and mCIMT on previously acquired images of the right common carotid artery of 200 consecutive patients. Measurements were performed twice, four weeks apart; sCIMT was reported along with an image quality index (IQI) provided by the software. CVR stratification was compared for thresholds established by mCIMT studies, adapted for sCIMT according to a regression model.Results: sCIMT (median 0.67 mm, interquartile range [IQR] 0.57‒0.76 mm) was significantly lower (p<0.001) than mCIMT (median 0.76 mm, IQR 0.63‒0.84 mm; ρ=0.832, p<0.001, slope 0.714, intercept 0.124). Overall, intra-reader reproducibility was 76% for sCIMT and 83% for mCIMT (p=0.002), inter-reader reproducibility 75% and 76%, respectively (p=0.316). In 129 cases with IQI≥0.65, reproducibility was significantly higher (p≤0.004) for sCIMT than for mCIMT (intra-reader 85% versus 83%, inter-reader 80% versus 77%,). The agreement between sCIMT and mCIMT for CVR stratification was fair both overall (κ=0.270) and for IQI≥0.65 (κ=0.345), crude concordance being 79% and 88%, respectively.Conclusions: Reproducibility of sCIMT was not higher than mCIMT overall but sCIMT was significantly more reproducible than mCIMT for high-IQI cases. sCIMT cannot be used for CVR stratification due to fair concordance with mCIMT, even for high IQI. More research is required to improve image quality and define sCIMT-based thresholds for stratification of CVR.
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Laurinavicius, Arvydas, Aida Laurinaviciene, Darius Dasevicius, Nicolas Elie, Benoît Plancoulaine, Catherine Bor, and Paulette Herlin. "Digital Image Analysis in Pathology: Benefits and Obligation." Analytical Cellular Pathology 35, no. 2 (2012): 75–78. http://dx.doi.org/10.1155/2012/243416.
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Pathology has recently entered the era of personalized medicine. This brings new expectations for the accuracy and precision of tissue-based diagnosis, in particular, when quantification of histologic features and biomarker expression is required. While for many years traditional pathologic diagnosis has been regarded as ground truth, this concept is no longer sufficient in contemporary tissue-based biomarker research and clinical use. Another major change in pathology is brought by the advancement of virtual microscopy technology enabling digitization of microscopy slides and presenting new opportunities for digital image analysis. Computerized vision provides an immediate benefit of increased capacity (automation) and precision (reproducibility), but not necessarily the accuracy of the analysis. To achieve the benefit of accuracy, pathologists will have to assume an obligation of validation and quality assurance of the image analysis algorithms. Reference values are needed to measure and control the accuracy. Although pathologists' consensus values are commonly used to validate these tools, we argue that the ground truth can be best achieved by stereology methods, estimating the same variable as an algorithm is intended to do. Proper adoption of the new technology will require a new quantitative mentality in pathology. In order to see a complete and sharp picture of a disease, pathologists will need to learn to use both their analogue and digital eyes.
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Espinasse, Mathilde, Stéphanie Pitre-Champagnat, Benoit Charmettant, Francois Bidault, Andreas Volk, Corinne Balleyguier, Nathalie Lassau, and Caroline Caramella. "CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review." Diagnostics 10, no. 5 (April 28, 2020): 258. http://dx.doi.org/10.3390/diagnostics10050258.
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Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.
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Zhovannik, Ivan, Dennis Bontempi, Alessio Romita, Elisabeth Pfaehler, Sergey Primakov, Andre Dekker, Johan Bussink, Alberto Traverso, and René Monshouwer. "Segmentation Uncertainty Estimation as a Sanity Check for Image Biomarker Studies." Cancers 14, no. 5 (March 2, 2022): 1288. http://dx.doi.org/10.3390/cancers14051288.
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Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lack reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients, respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure of less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on the Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability of segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p = 0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty, to effect ratio, only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with a log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain in its predictions for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation, without considering segmentation, uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing).
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van der Laak, Jeroen A. W. M., Albertus G. Siebers, Sabine A. A. P. Aalders, Johanna M. M. Grefte, Peter C. M. de Wilde, and Johan Bulten. "Objective Assessment of Cancer Biomarkers Using Semi-Rare Event Detection." Analytical Cellular Pathology 29, no. 6 (January 1, 2007): 483–95. http://dx.doi.org/10.1155/2007/487435.
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Objective and reproducible assessment of cancer biomarkers may be performed using rare event detection systems. Because many biomarkers are not true ‘rare events’, in this study a semi-rare event detection system was developed. The system is capable of assigning a discriminant score to detected positive cells, expressing the extent and intensity of the immunocytochemical staining. A gallery image is constructed showing the diagnostically most interesting cells as well as quantitative data expressing the biomarker staining pattern. To increase scanning speed, an adaptive scanning strategy is studied in which scanning is aborted when a sufficient number of positive cells has been identified. System performance was evaluated using liquid based cervical smears, stained with an antibody directed against p16INK4a tumor suppressor protein. Overexpression of p16INK4a in cervix is related to high-risk HPV infection, which is associated with carcinogenesis. Reproducibility of the system was tested on specimens containing limited positivity. Quantitative analysis was evaluated using 10 cases within normal limits and 10 high grade lesions. The system was highly reproducible in detecting positive cells and in calculating discriminant scores (average CV 0.7%). Quantitative features were significantly increased in high grade lesions (p < 0.001). Adaptive scanning decreased scanning time with only minor impact on scanning results. The system is capable of automated, objective and reproducible assessment of biomarker expression and may be useful for a variety of applications.
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Boellaard, Ronald, Roberto Delgado-Bolton, Wim J. G. Oyen, Francesco Giammarile, Klaus Tatsch, Wolfgang Eschner, Fred J. Verzijlbergen, et al. "FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0." European Journal of Nuclear Medicine and Molecular Imaging 42, no. 2 (December 2, 2014): 328–54. http://dx.doi.org/10.1007/s00259-014-2961-x.
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Abstract The purpose of these guidelines is to assist physicians in recommending, performing, interpreting and reporting the results of FDG PET/CT for oncological imaging of adult patients. PET is a quantitative imaging technique and therefore requires a common quality control (QC)/quality assurance (QA) procedure to maintain the accuracy and precision of quantitation. Repeatability and reproducibility are two essential requirements for any quantitative measurement and/or imaging biomarker. Repeatability relates to the uncertainty in obtaining the same result in the same patient when he or she is examined more than once on the same system. However, imaging biomarkers should also have adequate reproducibility, i.e. the ability to yield the same result in the same patient when that patient is examined on different systems and at different imaging sites. Adequate repeatability and reproducibility are essential for the clinical management of patients and the use of FDG PET/CT within multicentre trials. A common standardised imaging procedure will help promote the appropriate use of FDG PET/CT imaging and increase the value of publications and, therefore, their contribution to evidence-based medicine. Moreover, consistency in numerical values between platforms and institutes that acquire the data will potentially enhance the role of semiquantitative and quantitative image interpretation. Precision and accuracy are additionally important as FDG PET/CT is used to evaluate tumour response as well as for diagnosis, prognosis and staging. Therefore both the previous and these new guidelines specifically aim to achieve standardised uptake value harmonisation in multicentre settings.
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Mosher, Timothy J., Zheng Zhang, Ravinder Reddy, Sanaa Boudhar, Barton N. Milestone, William B. Morrison, C. Kent Kwoh, Felix Eckstein, Walter R. T. Witschey, and Arijitt Borthakur. "Knee Articular Cartilage Damage in Osteoarthritis: Analysis of MR Image Biomarker Reproducibility in ACRIN-PA 4001 Multicenter Trial." Radiology 258, no. 3 (March 2011): 832–42. http://dx.doi.org/10.1148/radiol.10101174.
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Ram, Sripad, and Eric Powell. "54 A strategy to quantitatively assess the accuracy and precision of multiplex immunofluorescence assays – application to Ultivue Insituplex® PD-L1, T-act and APC panels." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A61. http://dx.doi.org/10.1136/jitc-2021-sitc2021.054.
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BackgroundMultiplex immunofluorescence assays represent an essential tool in immuno-oncology research. The recent past has witnessed the introduction of several multiplexing methodologies to detect multiple biomarkers on a single tissue section. The quantitative assessment of the accuracy and precision of multiplex panels is of paramount importance for their widespread use in clinical samples. While there have been numerous reports providing qualitative characterization of multiplex panels, there is a paucity of data concerning the quantitative validation of these panels.MethodsUltivue Insituplex® T-act, PD-L1 and APC panels, which are 4-plex assays, were evaluated using breast tumor resections with varying levels of T-cell infiltration. For each panel, accuracy and precision was evaluated through a concordance and a reproducibility test, respectively. In the concordance test, five serial sections from each tumor specimen were cut and the 3rd (middle) serial section was immunolabeled with the 4-plex assay whereas the remaining sections were immunolabeled for the individual biomarkers (1-plex) that comprised the 4-plex assay. In the reproducibility test, five serial sections from each tumor specimen were immunolabeled with the 4-plex assay in separate, independent runs. The coefficient of variation (CV) of the density of different cell phenotypes was quantified from the serial sections and was used to assess the precision of that multiplex panel. All whole-slide image analysis was performed in QuPath software (version 0.2.3).ResultsThe results of the concordance test revealed that the relative difference in the single-biomarker cell density between 1-plex and 4-plex assays for the biomarkers in the 3 panels was typically less than 25%. Results of the precision test revealed that the CV for most cell phenotypes was typically less than 30%. We also identified special phenotypes such as CD3+PanCK+ cells and CD3+CD68+ cells, which exhibited unexpected combinations of biomarkers. Additional analysis revealed that these special phenotypes were in fact pairs of touching cells that were positive for the corresponding individual biomarkers (e.g., CD3+ cell touching a PanCK+ cell), and that this was due to limitations in the image analysis software package to segment the touching nuclei as two separate entities.ConclusionsOur results demonstrated that the Ultivue panels evaluated here had satisfactory accuracy and precision in breast tumor resections. The identification of special cell phenotypes in our data revealed the potential shortcomings of image analysis software and underscored the importance of performing a comprehensive evaluation of the multiplex assay as well as the image analysis workflow.Ethics ApprovalThe biospecimens used in the study were anonymized specimens which were collected with written patient consent, processed and distributed in full ethical and regulatory compliance with the Sites from which they were collected. This includes independent ethical review, Institutional Review Board approval (where appropriate), and independent regulatory review.
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Boyaci, Ceren, Wenwen Sun, Stephanie Robertson, Balazs Acs, and Johan Hartman. "Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group." Biomolecules 11, no. 11 (October 30, 2021): 1612. http://dx.doi.org/10.3390/biom11111612.
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Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920–0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593–4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.
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Patel, Bhavika, Stephanie Allen, Brittney Boldt, Noah Ramirez, Najiba Mammadova, Agnes Haggerty, and Navi Mehra. "Whole-slide multispectral imaging reveals the immune subtypes of melanoma associated with the tumor microenvironment: An automated 7-color mIF assay." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e21591-e21591. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e21591.
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e21591 Background: Immunotherapy and precision medicine are rapidly developing approaches to cancer therapy. Biomarkers that detect the tumor and tumor microenvironment allow for the development of strategies that accelerate the advancement of treatments to enhance a patient’s immune system. Akoya’s MOTiF PD-1/PD-L1 Auto Melanoma Kit is a validated, multiplex immunoassay enabling detection of the 6 most clinically relevant immuno-oncology and spatial biomarkers: FoxP3, PD-L1, Sox10/S100, PD-1, CD8 and CD68. Methods: In this study, the MOTiF PD-1/PD-L1 panel was used to analyze the tumor microenvironment and specifically assess immune phenotypes within melanoma samples from 3 patients. This study demonstrates a fully optimized and end-to-end workflow solution for biomarker discovery in melanoma. Results: We demonstrate the utility of Akoya’s MOTiF PD-1/PD-L1 Melanoma panel kit in studying the cellular diversity while retaining spatial context. Stained slides were analyzed using the InForm and Phenoptr Reports image analysis platforms to identify phenotypes and better understand spatial relationships between cell phenotypes. The MOTiF PD-1/PD-L1 panel kit provides reproducibility across different patient samples. Conclusions: This data provides insight into the innate and adaptive immune landscape for targeted design of new immunotherapies as well as improved efficacy and reduced toxicity in the treatment of melanoma.
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Mehra, Navi, Bhavika Patel, Stephanie Allen, Brittney Boldt, Noah Ramirez, Najiba Mammadova, and Agnes Haggerty. "Whole-slide multispectral imaging reveals the immune subtypes of melanoma associated with the tumor microenvironment: An automated 7-color mIF assay." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 48.16. http://dx.doi.org/10.4049/jimmunol.208.supp.48.16.
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Abstract Immunotherapy and precision medicine are rapidly developing approaches to cancer therapy. Biomarkers that detect the tumor and tumor microenvironment allow for the development of strategies that accelerate the advancement of treatments to enhance a patient’s immune system. Akoya’s MOTiF™ PD-1/PD-L1 Auto Melanoma Kit is a validated, multiplex immunoassay enabling detection of the 6 most clinically relevant immuno-oncology and spatial biomarkers: FoxP3, PD-L1, Sox10/S100, PD-1, CD8 and CD68. In this study, the MOTiF™ PD-1/PD-L1 panel was used to analyze the tumor microenvironment and specifically assess immune phenotypes within melanoma samples from 3 patients. This study demonstrates a fully optimized and end-to-end workflow solution for biomarker discovery in melanoma. We demonstrate the utility of Akoya’s MOTiF™ PD-1/PD-L1 Melanoma panel kit in studying the cellular diversity while retaining spatial context. Stained slides were analyzed using the InForm® and PhenoptrReports image analysis platforms to identify phenotypes and better understand spatial relationships between cell phenotypes. The MOTiF™ PD-1/PD-L1 panel kit provides reproducibility across different patient samples. This data provides insight into the innate and adaptive immune landscape for targeted design of new immunotherapies as well as improved efficacy and reduced toxicity in the treatment of melanoma.
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Tamal, Mahbubunnabi. "A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET." Applied Sciences 11, no. 2 (January 7, 2021): 535. http://dx.doi.org/10.3390/app11020535.
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Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.
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Tamal, Mahbubunnabi. "A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET." Applied Sciences 11, no. 2 (January 7, 2021): 535. http://dx.doi.org/10.3390/app11020535.
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Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.
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Ceschin, Rafael, Ashok Panigrahy, and Vanathi Gopalakrishnan. "sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping." Cancer Informatics 14s2 (January 2015): CIN.S17293. http://dx.doi.org/10.4137/cin.s17293.
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A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.
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Ortega, Francisco Gabriel, Germán E. Gómez, Coral González-Martinez, Teresa Valero, José Expósito-Hernández, Ignacio Puche, Alba Rodriguez-Martinez, María José Serrano, José Antonio Lorente, and Martín A. Fernández-Baldo. "A Novel, Quick, and Reliable Smartphone-Based Method for Serum PSA Quantification: Original Design of a Portable Microfluidic Immunosensor-Based System." Cancers 14, no. 18 (September 16, 2022): 4483. http://dx.doi.org/10.3390/cancers14184483.
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We describe a versatile, portable, and simple platform that includes a microfluidic electrochemical immunosensor for prostate-specific antigen (PSA) detection. It is based on the covalent immobilization of the anti-PSA monoclonal antibody on magnetic microbeads retained in the central channel of a microfluidic device. Image flow cytometry and scanning electron microscopy were used to characterize the magnetic microbeads. A direct sandwich immunoassay (with horseradish peroxidase-conjugated PSA antibody) served to quantify the cancer biomarker in serum samples. The enzymatic product was detected at −100 mV by amperometry on sputtered thin-film electrodes. Electrochemical reaction produced a current proportional to the PSA level, with a linear range from 10 pg mL−1 to 1500 pg mL−1. The sensitivity was demonstrated by a detection limit of 2 pg mL−1 and the reproducibility by a coefficient of variation of 6.16%. The clinical performance of this platform was tested in serum samples from patients with prostate cancer (PCa), observing high specificity and full correlation with gold standard determinations. In conclusion, this analytical platform is a promising tool for measuring PSA levels in patients with PCa, offering a high sensitivity and reduced variability. The small platform size and low cost of this quantitative methodology support its suitability for the fast and sensitive analysis of PSA and other circulating biomarkers in patients. Further research is warranted to verify these findings and explore its potential application at all healthcare levels.
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Squatrito, Marlyne, Silvia Blacher, Laurie Henry, Soraya Labied, Agnès Noel, Michelle Nisolle, and Carine Munaut. "Comparison of Morphological and Digital-Assisted Analysis for BCL6 Endometrial Expression in Women with Endometriosis." Journal of Clinical Medicine 11, no. 20 (October 19, 2022): 6164. http://dx.doi.org/10.3390/jcm11206164.
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BCL6 (B-cell lymphoma 6) is a proto-oncogene and transcriptional repressor initially described as being involved in B-cell lymphoma. Recently, this factor has been identified as a promising tissue biomarker which could be used to diagnose women affected by endometriosis. Previous studies used HSCORE for BCL6 staining quantification in the endometrium. However, this semi-quantitative technique of analysis has some limitations, including a lack of objectivity, robustness, and reproducibility that may lead to intra- and inter-observer variability. Our main goal was to develop an original computer-assisted method to quantify BCL6 staining from whole-slide images reliably. In order to test the efficiency of our new digital method of quantification, we compared endometrial BCL6 expression between fertile and infertile women without or with different stages of endometriosis by using the widely used HSCORE analysis and our new automatic digital image analysis. We find a higher expression of BCL6 in the endometrium of infertile women with endometriosis and women with stage IV endometriosis. Furthermore, we demonstrate a significant correlation between the two types of independent measurements, indicating the robustness of results and also the reliability of our computer-assisted method for BCL6 quantification. In conclusion, our work, by using this original computer-assisted method, enables BCL6 quantification more objectively, reliably, robustly, and promptly compared to HSCORE analysis.
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Patel, Bhavika, Stephanie Allen, Brittney Boldt, Najiba Mammadova, Agnes Haggerty, and Navi Mehra. "Abstract 2768: Whole-slide multispectral imaging reveals the immune subtypes of melanoma associated with the tumor microenvironment." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2768. http://dx.doi.org/10.1158/1538-7445.am2022-2768.
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Abstract Immunotherapy and precision medicine are rapidly developing approaches to cancer therapy. Biomarkers that detect the tumor and tumor microenvironment allow for the development of strategies that accelerate the advancement of treatments to enhance a patient’s immune system. Akoya’s MOTiF™ PD-1/PD-L1 Auto Melanoma Kit is a validated, multiplex immunoassay enabling detection of the 6 most clinically relevant immuno-oncology and spatial biomarkers: FoxP3, PD-L1, Sox10/S100, PD-1, CD8 and CD68. In this study, the MOTiF™ PD-1/PD-L1 panel was used to analyze the tumor microenvironment and specifically assess immune phenotypes within melanoma samples from 3 patients. This study demonstrates a fully optimized and end-to-end workflow solution for biomarker discovery in melanoma. We demonstrate the utility of Akoya’s MOTiF™ PD-1/PD-L1 Melanoma panel kit in studying the cellular diversity while retaining spatial context. Stained slides were analyzed using the InForm® and PhenoptrReports image analysis platforms to identify phenotypes and better understand spatial relationships between cell phenotypes. The MOTiF™ PD-1/PD-L1 panel kit provides reproducibility across different patient samples. This data provides insight into the innate and adaptive immune landscape for targeted design of new immunotherapies as well as improved efficacy and reduced toxicity in the treatment of melanoma. Citation Format: Bhavika Patel, Stephanie Allen, Brittney Boldt, Najiba Mammadova, Agnes Haggerty, Navi Mehra. Whole-slide multispectral imaging reveals the immune subtypes of melanoma associated with the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2768.
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Fanciullo, Cristiana, Salvatore Gitto, Eleonora Carlicchi, Domenico Albano, Carmelo Messina, and Luca Maria Sconfienza. "Radiomics of Musculoskeletal Sarcomas: A Narrative Review." Journal of Imaging 8, no. 2 (February 13, 2022): 45. http://dx.doi.org/10.3390/jimaging8020045.
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Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.
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Pollan, Sara, Bethany Remeniuk, Arezoo Hanifi, Kristin Roman, Bei Hopkins, Natalie Monteiro, Harry Nunns, Erinn Parnell, Josette William, and Qingyan Au. "51 A novel cross-site analysis of Vectra® Polaris™ multiplex fluorescence PD-1/PD-L1 immunohistochemistry on colorectal cancer with high and low microsatellite instability." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A58. http://dx.doi.org/10.1136/jitc-2021-sitc2021.051.
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BackgroundColorectal cancer (CRC) is the third most diagnosed cancer in the United States with a projected 52,980 deaths in 2021.1 Microsatellite instability-high (MSI-H) CRCs with deficiencies in mismatch repair (MMR) are significantly associated with positive response to immunotherapy and improved outcomes when treated with immune checkpoint inhibitors. Programmed cell death ligand-1 (PD-L1) is an effective biomarker of MSI-H status to identify CRC patients who will respond to treatment, however, reproducible quantification of programmed cell death receptor-1 (PD-1)/PD-L1 in the tumor microenvironment (TME) across laboratory sites has been under-reported.2–3 In this study, our group directly addressed this issue by interrogating PD-1/PD-L1 cross-site at Akoya Biosciences and NeoGenomics Laboratories by employing the MOTiF™ PD-1/PD-L1 Panel kit along with the Vectra Polaris imaging system.MethodsSerial sections from 40 CRC samples with known MSI status were stained at Akoya and NeoGenomics Laboratories using a modified MOTiF PD-1/PD-L1 Lung Panel Kit on the Leica BOND RX. Sections were scanned using the Vectra Polaris imaging system at both sites. Inter-site staining reproducibility was assessed using image analysis algorithms developed with inForm tissue analysis software. Cell counts and densities were calculated using the R-script package PhenoptrReports and correlations were plotted per marker.ResultsThe average signal intensity for all markers/Opal fluorophores was within the recommended ranges of 10–30 normalized counts, with the exception of Polaris 780, which has an advised range of 1–10. This indicates the protocol stained successfully and reproducibly across all serial sections at both sites. Inter-site concordance analysis of cell densities for each marker yielded an average R2 value of ≥0.70. H-Score of PD-L1 quantified at the cell membrane trended with MSI status (stable/high).ConclusionsThis study demonstrated that the MOTiF PD-1/PD-L1 Panel kit imaged in conjunction with the Vectra Polaris is not only a reliable assay that can be run across different sites, based on the concordant cross-site data, but that re-optimization of the kit allows for the assay panel to be successfully adapted to other cancers, such as CRC, which can then capture biological differences across a multitude of samples.ReferencesAmerican Cancer Society https://www.cancer.org/cancer/colon-rectal-cancer/about/key-statistics.htmlYi M, Jiao D, Xu H, Liu Q, Zhao W, Han X, et al. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Mol Cancer 2018;17(1):129Lemery S, Keegan P, Pazdur R. First FDA approval agnostic of cancer site - when a biomarker defines the indication. N Engl J Med 2017;377(15):1409–12.
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Beaumont, Hubert, Estanislao Oubel, Antoine Iannessi, and Dag Wormanns. "Reliability of imaging biomarkers for response assessment in advanced lung cancer: Influence of expertise and automation." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e13547-e13547. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e13547.
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e13547 Background: Image-based biomarkers play an important role in the assessment of the response to therapy. The value of imaging biomarkers relies on their reproducibility, which depends on the reviewer and on the measuring system. This study aims at evaluating the impact of readers’ expertise and automation of measurements. Methods: A retrospective study was performed on 10 patients with at least one Non-Small Cell Lung Cancer (NSCLC) lesion, and followed over time (7 time points in average) with Computed Tomography (CT). 2 expert radiologists (ERs) and 5 imaging scientists (ISs) measured the Longest Axial Diameter (LAD) and the volume (VOL) of each lesion at each time point. ERs and ISs segmented the lesions by using a proprietary software providing semi-automatic segmentation processing with manual adjustment. ISs performed an additional session using manual segmentation tools only. From each segmentation, VOL and LAD were automatically computed. The variability of the measurements was calculated by using standard statistics. The response to treatment was assessed according to RECIST thresholds for LAD and with +/-30% thresholds for volume. The inter-reader agreement was measured trough the Kappa coefficient. Finally, the reviewing time with and without automation was analyzed. Results: The use of automated tools by ISs reduced the standard deviation of LAD difference from 10.7% to 8.4%. The inter-reader agreement improved Kappa from 0.57 to 0.68 for LAD, and from 0.52 to 0.69 for VOL. The automation reduced the reviewing time by a factor 4 with respect to the manual assessment. No significant differences in variability were found between ISs and the first ER, but significant differences were observed with respect to the second ER. Conclusions: In a RECIST context, automation improved significantly inter-reader agreement. When using volume as a biomarker, automation not only improved the inter-reader agreement, but also decreased notably the reviewing time. No evidence was found about the influence of the expertise on the volume measurement. The difference in the lesions interpretation by the experts is a relevant factor to account for.
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Kapadia, Monesh, Mehrnoush Khojasteh, Margarita Kouzova, Carol Jones, Xiao-Meng Xu, Matthew T. Olson, Sarah Gladden, et al. "Abstract P1-02-17: Artificial intelligence-based whole slide scoring of nuclear breast cancer IHC markers Ki67, ER, and PR matches performance of manual clinical scoring." Cancer Research 82, no. 4_Supplement (February 15, 2022): P1–02–17—P1–02–17. http://dx.doi.org/10.1158/1538-7445.sabcs21-p1-02-17.
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Abstract Background: 2.1 million breast cancers are newly diagnosed each year. Current guidelines endorse routine testing for estrogen receptor (ER) and progesterone receptor (PR), while use of the Ki67 biomarker can provide additional prognostic value. All three biomarkers currently require quantitative evaluation using manual review of a glass slide, resulting in reproducibility issues across labs due to interpretative and scoring variabilities. Current on-market image analysis algorithms only offer limited field-of-view (FOV) that analyze a tiny fraction of the entire tissue. Whole slide image (WSI) analysis, in comparison, analyzes the entire tissue and, therefore, more closely mimics how pathologists assess these slides in clinical practice. In this work, we developed three deep learning artificial intelligence (AI) based algorithms for WSI analysis (IA) of digitized images from Ki67, ER, and PR stained slides that address these variabilities and allow pathologists across labs to consistently score at the same accuracy as selected expert labs. The complete software solution delivers high throughput analyzing whole-slide images in less than 2 minutes during pre-computation on conventional computer hardware and returning results on user-provided annotations in milliseconds. Methods: We assembled a benchmark validation data set of 312 breast cancer cases (100 Ki67, 102 ER, and 110 PR slides, stained at multiple sites) representative of breast cancer subtypes (i.e. ductal, lobular, mucinous, medullary, tubular), score (i.e. 0%-100% positivity), tumor grade (i.e. well, moderately, and poorly differentiated), and specimen type (i.e. biopsy and resection). Three pre-clinical validation studies were performed using the Roche uPath enterprise software and each of the ER, PR and Ki67 image analysis algorithms. A total of 6 pathologists participated in the study split into expert (n=3) and study (n=3) readers. A non-inferiority Ground-Truth (GT) study design was implemented in which the study and expert readers performed manual read (MR) followed by AI-assisted scoring. The expert manual scores were used as GT to which the readers’ manual and AI scores were compared for each marker and case. Results: The overall concordance rates between AI scores and expert GT was as follows: For Ki67, OPA=97.2% (95% CI: 94.0, 99.7), NPA=97.8% (95% CI: 93.4,100), and PPA=96.7% (95% CI: 91.3, 100), for ER, OPA=95.4% (CI:91.4,98.4), NPA=96.4% (CI:92.5,99.4), and PPA=94.4% (CI:87.4,100), and for PR, OPA=96.1% (95% CI:92.7,99.1), NPA=96.7% (95% CI:92.5,100), and PPA=95.6% (95% CI:89.9,100). The differences between AI and MR overall concordance rates (AI-MR) when compared to the expert GT were: for Ki67: OPA-diff=1.4% (2-sided 95% CI:-0.7,3.7), NPA-diff=3.8% (CI:0.6,7.8), PPA-diff=-1.0% (CI:-3.5,0.0), for ER: OPA-diff=-0.9% (CI:-3.3,1.0), NPA-diff=-0.1% (CI:-3.1,3.0), PPA-diff=-1.8% (CI:-6.2,0.0), and for PR: OPA-diff=-1.5% (CI:-3.9,0.6), NPA-diff=-2.4% (CI:-6.8,1.2), PPA-diff= -0.7%(CI:-2.8,1.1) using the cutoffs 20% (Ki67), 1% (ER), and 1% (PR) respectively. Conclusion: Our preliminary feasibility data shows that pathologists using WSI analysis assisted scoring was equivalent to manual scoring and an expert panel GT using a truly representative benchmark data set. Additionally, image analysis algorithms are known to provide high reproducibility and precision. We will provide those numbers at a later stage as they were not fully available at time of submission. Our results show the value and potential of deep learning technologies to improve the diagnosis and care of patients with breast cancer. Citation Format: Monesh Kapadia, Mehrnoush Khojasteh, Margarita Kouzova, Carol Jones, Xiao-Meng Xu, Matthew T. Olson, Sarah Gladden, Nancy Sapanara, Shalini Singh, Chen Chun Chen, Isaac Bai, Jim Ranger-Moore, Landon J. Inge, Uday Kurkure, Ipshita Bhattacharya, Margaret Zhao, Karel Zuiderveld, Chandana Chintakindi, Bryan Lopez, Christoph Guetter. Artificial intelligence-based whole slide scoring of nuclear breast cancer IHC markers Ki67, ER, and PR matches performance of manual clinical scoring [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-02-17.
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Armstrong, Andrew J., Lars Edenbrandt, Eva Bondesson, Aseem Anand, Orjan Nordle, Michael Anthony Carducci, and Michael J. Morris. "Phase 3 prognostic analysis of the automated bone scan index (aBSI) in men with bone-metastatic castration-resistant prostate cancer (CRPC)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 5006. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.5006.
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5006 Background: Quantitative measures of metastatic bone disease are needed in men with mCRPC. We recently demonstrated the validity/reproducibility of a computational approach to bone scan imaging that employs artificial intelligence called the automated BSI (aBSI), which quantifies the percent of skeletal mass involved by cancer. We aimed to extend the prognostic validation of aBSI in a multinational prospective phase 3 clinical study of men with bone-metastatic CRPC. Methods: Whole-body bone scans were acquired at screening in a placebo-controlled phase 3 trial of men with mCRPC and bone metastases and treated with tasquinimod/placebo (n = 1,245). The prospective aBSI biomarker analysis plan was locked in Sept 2014 prior to treatment unblinding. All scans generated at 241 trial sites in 37 countries were assessed for image quality and analyzed using the EXINI boneBSI v.2 software and were blindly associated with outcomes. Baseline aBSI was evaluated for its independent prognostic association with overall survival (OS), radiographic progression-free survival (rPFS), and symptomatic skeletal related events (SSEs). Results: The aBSI-population (721 pts) was representative of the entire trial population based on patient characteristics at screening and OS outcomes. Median aBSI was 1.07 (SE 0.05). The aBSI-population was divided into quartiles (n = 180-181) with aBSI-levels of 0 - 0.3 (Q1); > 0.3 - 1.1 (Q2); > 1.1 - 4.0 (Q3); and > 4.0 (Q4) and median OS ranging from 35 months (Q1) to 13 mo (Q4) (p < 0.0001). Baseline aBSI was significantly associated with OS (HR 1.2 per doubling of BSI; p < 0.0001) and remained independently associated with OS after adjustment for treatment, PSA, CRP, LDH and albumin. Baseline aBSI was also strongly associated with rPFS (p = 0.0005), time to symptomatic progression (p < 0.0001), and time to SSE (p = 0.001). Conclusions: This analysis represents the first phase 3 evaluation of aBSI as a clinically validated prognostic biomarker for OS, rPFS, and SSEs in men with bone-metastatic CRPC, providing independent prognostic information over commonly measured clinical characteristics. Clinical trial information: NCT01234311.
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Santos, James Paul, Jacob Levy, Kristen Ruma, David Yang, Steve Woolfenden, Jimmie Lim, Xun Li, et al. "Abstract 1725: Tissue-based biomarker analysis of DLBCL using multiplex immunofluorescence AQUA (Automated Quantitative Analysis) algorithms." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1725. http://dx.doi.org/10.1158/1538-7445.am2022-1725.
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Abstract Purpose: Deeper understanding of immune landscape of DLBCL tumor microenvironment is critical for exploration and development of next generation immunotherapies. Although conventional immunohistochemical (IHC) analysis can provide information on the immune landscape or target expression, it is generally limited to single marker analysis. Advantages of multiplex fluorescence IHC (mFIHC) include the ability to assess different cell types, their spatial relationship as well as variable antigen expression on tumor cells. As such, we developed quantitative mFIHC panels using AQUA® (Automated Quantitative Analysis) technology to evaluate T and B cell populations and their functional status to generate detailed spatial information of immune checkpoint (IC) markers. mFIHC combined with hypothesis driven spatial profiling algorithms (e.g., AQUA Technology) were found to provide the most powerful predictors of immunotherapies in a systematic meta-analyses of over 8000 patients treated with PD1/L1 pathway blockers (Lu et al., JAMA Oncol 2019). Implementation of mFIHC coupled with robust image analysis may provide great insight into DLBCL immune surveillance, mechanism of resistance and patient stratification. Study Design: We designed two novel mFIHC assays to (1) characterize various B cell populations (CD19, CD20, CD22, PAX5, CD3, TIM3), and (2) evaluate T cell functional status (CD8, Granzyme B, PD1, PDL1, TIM3, Tumor). We successfully validated clinical grade mFIHC assays using automated staining (Leica Bond RX), imaging (Vectra Polaris) and analyses (AQUA Technology) workflow. Results: Sensitivity, accuracy and specificity were confirmed for all mFIHC assays on known positive and negative controls. Excellent reproducibility (less than 35% CV) and precision were observed across instruments, operators and independent experiments for all markers. Between these two panels, over 200 unique parameters were evaluated. The prevalence of CD19, CD20, CD22, and PAX5 ranged from 30% to >90% positive for the DLBCL samples tested and were overall highly concordant with each other. Conclusion: The validated mFIHC assays examine B cell antigen expression in DLBCL and their interaction with CD3+ T cells and further characterize CD8+ T cells and immune checkpoint expression. These panels may be used to further understand the complex immune cell and tumor cell spatial biology in the context of clinical trials. Citation Format: James Paul Santos, Jacob Levy, Kristen Ruma, David Yang, Steve Woolfenden, Jimmie Lim, Xun Li, Ariana Valencia, James Deeds, Ramu Thiruvamoor, Xin Li, Emmanuel Pacia, Margaret McLaughlin, Thai Tran, Sarah Choi, Jennifer Bordeaux. Tissue-based biomarker analysis of DLBCL using multiplex immunofluorescence AQUA (Automated Quantitative Analysis) algorithms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1725.
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Arias-Alpízar, Kevin, Ana Sánchez-Cano, Judit Prat-Trunas, Elena Sulleiro, Pau Bosch-Nicolau, Fernando Salvador, Inés Oliveira, Israel Molina, Adrián Sánchez-Montalvá, and Eva Baldrich. "Magnetic Bead Handling Using a Paper-Based Device for Quantitative Point-of-Care Testing." Biosensors 12, no. 9 (August 25, 2022): 680. http://dx.doi.org/10.3390/bios12090680.
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Microfluidic paper-based analytical devices (μPADs) have been extensively proposed as ideal tools for point-of-care (POC) testing with minimal user training and technical requirements. However, most μPADs use dried bioreagents, which complicate production, reduce device reproducibility and stability, and require transport and storage under temperature and humidity-controlled conditions. In this work, we propose a μPAD produced using an affordable craft-cutter and stored at room temperature, which is used to partially automate a single-step colorimetric magneto-immunoassay. As a proof-of-concept, the μPAD has been applied to the quantitative detection of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), a biomarker of malaria infection. In this system, detection is based on a single-step magneto-immunoassay that consists of a single 5-min incubation of the lysed blood sample with immuno-modified magnetic beads (MB), detection antibody, and an enzymatic signal amplifier (Poly-HRP). This mixture is then transferred to a single-piece paper device where, after on-chip MB magnetic concentration and washing, signal generation is achieved by adding a chromogenic enzyme substrate. The colorimetric readout is achieved by the naked eye or using a smartphone camera and free software for image analysis. This μPAD afforded quantitative Pf-LDH detection in <15 min, with a detection limit of 6.25 ng mL−1 when the result was interpreted by the naked eye and 1.4 ng mL−1 when analysed using the smartphone imaging system. Moreover, the study of a battery of clinical samples revealed concentrations of Pf-LDH that correlated with those provided by the reference ELISA and with better sensitivity than a commercial rapid diagnostic test (RDT). These results demonstrate that magneto-immunoassays can be partly automated by employing a μPAD, achieving a level of handling that approaches the requirements of POC testing.
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Naghizadeh, Alireza, Wei-chung Tsao, Jong Hyun Cho, Hongye Xu, Mohab Mohamed, Dali Li, Wei Xiong, Dimitri Metaxas, Carlos A. Ramos, and Dongfang Liu. "In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes." PLOS Computational Biology 18, no. 3 (March 18, 2022): e1009883. http://dx.doi.org/10.1371/journal.pcbi.1009883.
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The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development. Trial Registration: ClinicalTrials.gov NCT00881920.
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Alamsahebpour, A., G. Zadeh, and K. Aldape. "PS2 - 205 Application of Computer-Assisted Diagnostics for Immunohistochemistry Analysis of Gliomas." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 43, S4 (October 2016): S16—S17. http://dx.doi.org/10.1017/cjn.2016.377.
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In the current practice of pathology, the identification of cell markers and their respective distribution represents an indispensable dialogue for diagnostic, predictive, therapeutic, and research purposes. Early immunohistochemical protocols were limited to direct, fluorescent labeled antibodies, yielding quick results but lacking sensitivity. More recently, the use of indirect techniques –utilization of enzyme labels–and various detection systems have continued to advance the complexity of IHC, increasing both its specificity and sensitivity of detecting one or multiple antigen(s) (Ag) simultaneously. As such, IHC has become an affordable, powerful, and readily available means for the identification of candidate biomarkers (mostly lineage markers) in formalin-fixed, paraffin-embedded (FFPE) tissue samples. Pathologists are now asked to “quantify” expression levels of differential prognostic markers–at microscopic level–using this arguably “non-quantitative” technique. Conventionally, histological grading relies mainly on manual counting of positively immunostained cells, a labour intensive protocol that may be associated with subjectivity, intra- and inter- observer variation and reproducibility issues. The subjectivity and lack of reproducibility has prompted the use of computer-assisted or fully automated image analysis technologies. Digital image acquisition systems are becoming commonplace and as such, the demand for complex assessments of digital images of histological slides must be matched with quantitative platforms. In this study, we aim to introduce a computer-assisted image-computing platform that is both accurate and efficient in quantification of isolated and heterogeneous candidate biomarkers in glioblastoma.
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Bashyam, Vishnu M., Guray Erus, Jimit Doshi, Mohamad Habes, Ilya M. Nasrallah, Monica Truelove-Hill, Dhivya Srinivasan, et al. "MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide." Brain 143, no. 7 (June 27, 2020): 2312–24. http://dx.doi.org/10.1093/brain/awaa160.
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Abstract Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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Lee, Hee Jin, Soo Yoon Cho, Eun Yoon Cho, Yoojoo Lim, Soo Ick Cho, Wonkyung Jung, Sanghoon Song, et al. "Artificial intelligence (AI)–powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC)." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 595. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.595.
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595 Background: Stromal TIL are a well-recognized prognostic and predictive biomarker in breast cancer. There is a need for tools assisting visual assessment of TIL, to improve reproducibility as well as for convenience. This study aims to assess the clinical significance of AI-powered spatial TIL analysis in the prediction of pathologic complete response (pCR) after NAC in TNBC patients. Methods: H&E stained slides and clinical outcomes data were obtained from stage I – III TNBC patients treated with NAC in two centers in Korea. For spatial TIL analysis, we used Lunit SCOPE IO, an AI-powered H&E Whole-Slide Image (WSI) analyzer, which identifies and quantifies TIL within the cancer or stroma area. Lunit SCOPE IO was developed with a 13.5 x 109 micrometer2 area and 6.2 x 106 TIL from 17,849 H&E WSI of multiple cancer types, annotated by 104 board-certified pathologists. iTIL score and sTIL score were defined as area occupied by TIL in the intratumoral area (%) and the surrounding stroma (%), respectively. Immune phenotype (IP) of each slide was defined from spatial TIL calculation, as inflamed (high TIL density in tumor area), immune-excluded (high TIL density in stroma), or desert (low TIL density overall). Results: A total of 954 TNBC patients treated from 2006 to 2019 were included in this analysis. pCR (ypT0N0) was confirmed in 261 (27.4%) patients. The neoadjuvant regimens used were mostly anthracycline (97.8%) and taxane (75.1%) -based, with 116 (12.1%) patients receiving additional platinum and 41 (4.3%) patients treated as part of immune checkpoint inhibitor or PARP inhibitor clinical trials. The median iTIL score and sTIL score were 4.3% (IQR 3.2 – 5.8) and 8.1% (IQR 6.3 – 13.4), respectively. The mean iTIL score was significantly higher in patients who achieved pCR after NAC (5.8% vs. 4.5%, p < 0.001), and a similar difference was observed with sTIL score (12.1%.1 vs. 9.4%, p < 0.001). iTIL score was found to remain as an independent predictor of pCR along with cT stage and Ki-67 in the multivariable analysis (adjusted odds ratio 1.211 (95% CI 1.125 – 1.304) per 1 point (%) change in the score, p <0.001). By IP groups, 291 (30.5%) patients were classified as inflamed, 502 (52.6%) as excluded, and 161 (16.9%) as desert phenotype. The patients with inflamed phenotype were more likely to achieve pCR (44.7%) than other phenotypes (19.8%, p < 0.001). Conclusions: AI-powered spatial TIL analysis could assess TIL densities in the cancer area and surrounding stroma of TNBC, and TIL density scores and IP classification could predict pCR after NAC.
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Barnes, Michael, Chukka Srinivas, Isaac Bai, Judith Frederick, Wendy Liu, Anindya Sarkar, Xiuzhong Wang, et al. "Whole tumor section quantitative image analysis maximizes between-pathologists’ reproducibility for clinical immunohistochemistry-based biomarkers." Laboratory Investigation 97, no. 12 (August 14, 2017): 1508–15. http://dx.doi.org/10.1038/labinvest.2017.82.
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Mu, Wei, Matthew B. Schabath, and Robert J. Gillies. "Images Are Data: Challenges and Opportunities in the Clinical Translation of Radiomics." Cancer Research 82, no. 11 (June 6, 2022): 2066–68. http://dx.doi.org/10.1158/0008-5472.can-22-1183.
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Abstract Radiomics provides an opportunity to uncover image-based biomarkers through the conversion and analysis of standard-of-care medical images into high-dimensional mineable data. In the last decade, thousands of studies have been published on different clinical applications, novel analysis algorithms, and the stability and reproducibility of radiomics. Despite this, interstudy comparisons are challenging because there is not a generally accepted analytic and reporting standard. The ability to compare and combine results from multiple studies using interoperative platforms is an essential component on the path toward clinical application. The NCI supported study from van Griethuysen and colleagues published in Cancer Research in 2017 proposed PyRadiomics: an open-source radiomics quantification platform for standardized image processing. Since released, it has become a frequently utilized analytic tool in the radiomics literature and has accelerated the capability of combining data from different studies. The subsequent challenge will be the design of multicenter trials with a fixed and immutable version of software, which is currently open-source, readily modified and freely distributed. Generally, this is accomplished with a commercial partner to navigate the regulatory processes. See related article by van Griethuysen and colleagues, Cancer Res 2017;77:e104–7.
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Arnould, Louis, Déa Haddad, Florian Baudin, Pierre-Henry Gabrielle, Marc Sarossy, Alain M. Bron, Behzad Aliahmad, and Catherine Creuzot-Garcher. "Repeatability and Reproducibility of Retinal Fractal Dimension Measured with Swept-Source Optical Coherence Tomography Angiography in Healthy Eyes: A Proof-of-Concept Study." Diagnostics 12, no. 7 (July 21, 2022): 1769. http://dx.doi.org/10.3390/diagnostics12071769.
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The retinal vascular network fractal dimension (FD) could be a promising imaging biomarker. Our objective was to evaluate its repeatability and reproducibility in healthy eyes. A cross-sectional study was undertaken with young, healthy volunteers who had no reported cardiac risk factors or ocular disease history. For each participant, three SS-OCTA images (12 × 12 mm) were acquired using the Plex Elite 9000 (Carl Zeiss Meditec AG, Jena, Germany) by two ophthalmologists. Automated segmentation was obtained from both the superficial and deep capillary plexuses. FD was estimated by box counting. The intraclass correlation coefficients (ICC) were used as measures for repeatability and reproducibility. A total of 43 eyes of healthy volunteers were included. The mean ± standard deviation (SD) age was 30 ± 6.2 years. The results show good repeatability. The ICC was 0.722 (95% CI, 0.541–0.839) in the superficial capillary plexus and 0.828 (95% CI, 0.705–0.903) in the deep capillary plexus. For reproducibility, the ICC was 0.651 (95% CI, 0.439–0.795) and 0.363 (95% CI, 0.073–0.596) at the superficial and deep capillary plexus, respectively. In this study, the FD of the vascular network measured via SS-OCTA showed good repeatability and reproducibility in healthy participants.
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Taube, Janis M., Kristin Roman, Elizabeth L. Engle, Chichung Wang, Carmen Ballesteros-Merino, Shawn M. Jensen, John McGuire, et al. "Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study." Journal for ImmunoTherapy of Cancer 9, no. 7 (July 2021): e002197. http://dx.doi.org/10.1136/jitc-2020-002197.
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BackgroundEmerging data suggest predictive biomarkers based on the spatial arrangement of cells or coexpression patterns in tissue sections will play an important role in precision immuno-oncology. Multiplexed immunofluorescence (mIF) is ideally suited to such assessments. Standardization and validation of an end-to-end workflow that supports multisite trials and clinical laboratory processes are vital. Six institutions collaborated to: (1) optimize an automated six-plex assay focused on the PD-1/PD-L1 axis, (2) assess intersite and intrasite reproducibility of staining using a locked down image analysis algorithm to measure tumor cell and immune cell (IC) subset densities, %PD-L1 expression on tumor cells (TCs) and ICs, and PD-1/PD-L1 proximity assessments.MethodsA six-plex mIF panel (PD-L1, PD-1, CD8, CD68, FOXP3, and CK) was rigorously optimized as determined by quantitative equivalence to immunohistochemistry (IHC) chromogenic assays. Serial sections from tonsil and breast carcinoma and non-small cell lung cancer (NSCLC) tissue microarrays (TMAs), TSA-Opal fluorescent detection reagents, and antibodies were distributed to the six sites equipped with a Leica Bond Rx autostainer and a Vectra Polaris multispectral imaging platform. Tissue sections were stained and imaged at each site and delivered to a single site for analysis. Intersite and intrasite reproducibility were assessed by linear fits to plots of cell densities, including %PDL1 expression by TCs and ICs in the breast and NSCLC TMAs.ResultsComparison of the percent positive cells for each marker between mIF and IHC revealed that enhanced amplification in the mIF assay was required to detect low-level expression of PD-1, PD-L1, FoxP3 and CD68. Following optimization, an average equivalence of 90% was achieved between mIF and IHC across all six assay markers. Intersite and intrasite cell density assessments showed an average concordance of R2=0.75 (slope=0.92) and R2=0.88 (slope=0.93) for breast carcinoma, respectively, and an average concordance of R2=0.72 (slope=0.86) and R2=0.81 (slope=0.68) for NSCLC. Intersite concordance for %PD-L1+ICs had an average R2 value of 0.88 and slope of 0.92. Assessments of PD-1/PD-L1 proximity also showed strong concordance (R2=0.82; slope=0.75).ConclusionsAssay optimization yielded highly sensitive, reproducible mIF characterization of the PD-1/PD-L1 axis across multiple sites. High concordance was observed across sites for measures of density of specific IC subsets, measures of coexpression and proximity with single-cell resolution.
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Peters, Dana C., Jérôme Lamy, Albert J. Sinusas, and Lauren A. Baldassarre. "Left atrial evaluation by cardiovascular magnetic resonance: sensitive and unique biomarkers." European Heart Journal - Cardiovascular Imaging 23, no. 1 (October 29, 2021): 14–30. http://dx.doi.org/10.1093/ehjci/jeab221.
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Abstract Left atrial (LA) imaging is still not routinely used for diagnosis and risk stratification, although recent studies have emphasized its importance as an imaging biomarker. Cardiovascular magnetic resonance is able to evaluate LA structure and function, metrics that serve as early indicators of disease, and provide prognostic information, e.g. regarding diastolic dysfunction, and atrial fibrillation (AF). MR angiography defines atrial anatomy, useful for planning ablation procedures, and also for characterizing atrial shapes and sizes that might predict cardiovascular events, e.g. stroke. Long-axis cine images can be evaluated to define minimum, maximum, and pre-atrial contraction LA volumes, and ejection fractions (EFs). More modern feature tracking of these cine images provides longitudinal LA strain through the cardiac cycle, and strain rates. Strain may be a more sensitive marker than EF and can predict post-operative AF, AF recurrence after ablation, outcomes in hypertrophic cardiomyopathy, stratification of diastolic dysfunction, and strain correlates with atrial fibrosis. Using high-resolution late gadolinium enhancement (LGE), the extent of fibrosis in the LA can be estimated and post-ablation scar can be evaluated. The LA LGE method is widely available, its reproducibility is good, and validations with voltage-mapping exist, although further scan–rescan studies are needed, and consensus regarding atrial segmentation is lacking. Using LGE, scar patterns after ablation in AF subjects can be reproducibly defined. Evaluation of ‘pre-existent’ atrial fibrosis may have roles in predicting AF recurrence after ablation, predicting new-onset AF and diastolic dysfunction in patients without AF. LA imaging biomarkers are ready to enter into diagnostic clinical practice.
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Chinnasamy, Thiruppathiraja, Loes I. Segerink, Mats Nystrand, Jesper Gantelius, and Helene Andersson Svahn. "Point-of-Care Vertical Flow Allergen Microarray Assay: Proof of Concept." Clinical Chemistry 60, no. 9 (September 1, 2014): 1209–16. http://dx.doi.org/10.1373/clinchem.2014.223230.
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Abstract BACKGROUND Sophisticated equipment, lengthy protocols, and skilled operators are required to perform protein microarray-based affinity assays. Consequently, novel tools are needed to bring biomarkers and biomarker panels into clinical use in different settings. Here, we describe a novel paper-based vertical flow microarray (VFM) system with a multiplexing capacity of at least 1480 microspot binding sites, colorimetric readout, high sensitivity, and assay time of <10 min before imaging and data analysis. METHOD Affinity binders were deposited on nitrocellulose membranes by conventional microarray printing. Buffers and reagents were applied vertically by use of a flow controlled syringe pump. As a clinical model system, we analyzed 31 precharacterized human serum samples using the array system with 10 allergen components to detect specific IgE reactivities. We detected bound analytes using gold nanoparticle conjugates with assay time of ≤10 min. Microarray images were captured by a consumer-grade flatbed scanner. RESULTS A sensitivity of 1 ng/mL was demonstrated with the VFM assay with colorimetric readout. The reproducibility (CV) of the system was <14%. The observed concordance with a clinical assay, ImmunoCAP, was R2 = 0.89 (n = 31). CONCLUSIONS In this proof-of-concept study, we demonstrated that the VFM assay, which combines features from protein microarrays and paper-based colorimetric systems, could offer an interesting alternative for future highly multiplexed affinity point-of-care testing.
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Fan, Audrey P., Sindhuja T. Govindarajan, R. Philip Kinkel, Nancy K. Madigan, A. Scott Nielsen, Thomas Benner, Emanuele Tinelli, Bruce R. Rosen, Elfar Adalsteinsson, and Caterina Mainero. "Quantitative Oxygen Extraction Fraction from 7-Tesla MRI Phase: Reproducibility and Application in Multiple Sclerosis." Journal of Cerebral Blood Flow & Metabolism 35, no. 1 (October 29, 2014): 131–39. http://dx.doi.org/10.1038/jcbfm.2014.187.
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Quantitative oxygen extraction fraction (OEF) in cortical veins was studied in patients with multiple sclerosis (MS) and healthy subjects via magnetic resonance imaging (MRI) phase images at 7 Tesla (7 T). Flow-compensated, three-dimensional gradient-echo scans were acquired for absolute OEF quantification in 23 patients with MS and 14 age-matched controls. In patients, we collected T2∗-weighted images for characterization of white matter, deep gray matter, and cortical lesions, and also assessed cognitive function. Variability of OEF across readers and scan sessions was evaluated in a subset of volunteers. OEF was averaged from 2 to 3 pial veins in the sensorimotor, parietal, and prefrontal cortical regions for each subject (total of ∼10 vessels). We observed good reproducibility of mean OEF, with intraobserver coefficient of variation (COV)=2.1%, interobserver COV=5.2%, and scan—rescan COV=5.9%. Patients exhibited a 3.4% reduction in cortical OEF relative to controls ( P=0.0025), which was not different across brain regions. Although oxygenation did not relate with measures of structural tissue damage, mean OEF correlated with a global measure of information processing speed. These findings suggest that cortical OEF from 7-T MRI phase is a reproducible metabolic biomarker that may be sensitive to different pathologic processes than structural MRI in patients with MS.
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Ferrero, Giulio, Nicola Licheri, Lucia Coscujuela Tarrero, Carlo De Intinis, Valentina Miano, Raffaele Adolfo Calogero, Francesca Cordero, Michele De Bortoli, and Marco Beccuti. "Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data." International Journal of Molecular Sciences 21, no. 1 (December 31, 2019): 293. http://dx.doi.org/10.3390/ijms21010293.
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Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project.
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Pacia, Emmanuel, Xun Li, Ju Young Kim, Evelyn Diaz, Beiru Chen, Nathan Roscoe, Jason Hughes, et al. "47 Pathologists enhance interpretation of automated multiplex immunohistochemistry assays in cancer immunotherapy trials." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A50. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0047.
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BackgroundMultiplex fluorescence immunohistochemistry (mFIHC) enables simultaneous detection of multiple biomarkers on a single tissue section. Spatial patterns and differential expression of immune- and tumor cell biomarkers serve as powerful predictors of immunotherapies. In a recent meta-analyses of 8135 patients treated with PD1/L1 pathway blockers, mFIHC was found to provide highest predictive power (P<0.05) amongst commonly utilized biomarker modalities, namely, PD-L1 IHC, Tumor Mutation Burden and Gene Expression Profiling alone. [Lu et al., JAMA Oncol 2019]. As biomarkers in mFIHC assays are read by computer-aided algorithms, the role of pathologists in the digital workflow has been debated. Utilizing clinical cases representing multiple tumor indications, we illustrate the critical collaboration between pathologists (human intelligence, HI) and computer workflows (artificial intelligence, AI) required for accurate interpretation of mFIHC assays in cancer immunotherapy trials.MethodsIn our clinical trial laboratory, pathologists are involved in pre-analytical, analytical and post-analytical phases of clinical trial sample testing. In the pre-analytical phase, pathologist(s) perform histological examination of H&E stained tissue sections to annotate and confirm tissue types, diagnosis, tissue integrity and acceptance (including viable tumor component), followed by determination of Region of Interest (ROI) for subsequent analysis by computerized programs. In the analytical phase, pathologists identify specific areas of biological and/or clinical interest within ROI (tumor, non-tumor, invasive margin, and tumor-stromal interphase) in the computer scans, as well as exclude ROI containing necrosis, hemorrhage, blood vessels, and autofluorescence. Those pathologist-selected images are then quantified by digital pathology software such as Automated QUantitative Analyses (AQUA®) technology. Finally, pathologists also provide interpretation and summarize findings relevant to the clinical study during the post-analytical phase.ResultsCase studies representing distinct malignancies, such as melanoma, non-small cell lung cancer, squamous cell carcinoma of head and neck and diffuse large B-cell lymphoma, illustrating the role of pathologists and especially in rescuing challenging cases and interpreting biomarkers scores from mFIHC assays will be presented.ConclusionsWith the advancement in technologies to detect increasing number of biomarkers in a single tissue section and accompanied growth of mFIHC assays in immuno-oncology studies, there is a clear transition from conventional pathology (HI) to computer-aided pathology (AI+HI) that will ultimately ensure greater accuracy, reproducibility and standardization of clinical trial testing, and enable approval of more effective therapies and better patient care.
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Mori, Hidetoshi, Jennifer Bolen, Louis Schuetter, Pierre Massion, Clifford C. Hoyt, Scott VandenBerg, Laura Esserman, Alexander D. Borowsky, and Michael J. Campbell. "Characterizing the Tumor Immune Microenvironment with Tyramide-Based Multiplex Immunofluorescence." Journal of Mammary Gland Biology and Neoplasia 25, no. 4 (December 2020): 417–32. http://dx.doi.org/10.1007/s10911-021-09479-2.
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AbstractMultiplex immunofluorescence (mIF) allows simultaneous antibody-based detection of multiple markers with a nuclear counterstain on a single tissue section. Recent studies have demonstrated that mIF is becoming an important tool for immune profiling the tumor microenvironment, further advancing our understanding of the interplay between cancer and the immune system, and identifying predictive biomarkers of response to immunotherapy. Expediting mIF discoveries is leading to improved diagnostic panels, whereas it is important that mIF protocols be standardized to facilitate their transition into clinical use. Manual processing of sections for mIF is time consuming and a potential source of variability across numerous samples. To increase reproducibility and throughput we demonstrate the use of an automated slide stainer for mIF incorporating tyramide signal amplification (TSA). We describe two panels aimed at characterizing the tumor immune microenvironment. Panel 1 included CD3, CD20, CD117, FOXP3, Ki67, pancytokeratins (CK), and DAPI, and Panel 2 included CD3, CD8, CD68, PD-1, PD-L1, CK, and DAPI. Primary antibodies were first tested by standard immunohistochemistry and single-plex IF, then multiplex panels were developed and images were obtained using a Vectra 3.0 multispectral imaging system. Various methods for image analysis (identifying cell types, determining cell densities, characterizing cell-cell associations) are outlined. These mIF protocols will be invaluable tools for immune profiling the tumor microenvironment.
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Poulin, N., A. Frost, A. Carraro, E. Mommers, M. Guillaud, P. J. van Diest, W. Grizzle, and S. Beenken. "Risk Biomarker Assessment for Breast Cancer Progression: Replication Precision of Nuclear Morphometry." Analytical Cellular Pathology 25, no. 3 (2003): 129–38. http://dx.doi.org/10.1155/2003/262918.
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Nuclear morphometry is a method for quantitative measurement of histopathologic changes in the appearance of stained cell nuclei. Numerous studies have indicated that these assessments may provide clinically relevant information related to the degree of progression and malignant potential of breast neoplasia. Nuclear features are derived from computerized analysis of digitized microscope images, and a quantitative Feulgen stain for DNA was used. Features analyzed included: (1) DNA content; (2) nuclear size and shape; and (3) texture features, describing spatial features of chromatin distribution. In this study replicated measurements are described on a series of 54 breast carcinoma specimens of differing pathologic grades. Duplicate measurements were performed using two serial sections, which were processed and analyzed separately. The value of a single feature measurement, the nuclear area profile, was shown to be the strongest indicator of progression. A quantitative nuclear grade was derived and shown to be strongly correlated with not only the pathologic nuclear grade, but also with tubule formation, mitotic grade, and with the overall histopathologic grade. Analysis of replication precision showed that the standard methods of the histopathology laboratory, if practiced in a uniform manner, are sufficient to ensure reproducibility of these assessments. We argue that nuclear morphometry provides a standardized and reproducible framework for quantitative pathologic assessments.
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Giraldo-Castillo, Nicolas A., Sneha Berry, Julie Stein, Benjamin Green, Peter Nguyen, Abha Soni, Farah Succaria, et al. "Multiplex Immunofluorescence Image Cytometry Combined with Spatially-Resolved UMAP Defines Novel Immune Prognostic Biomarkers in Metastatic Melanoma." Journal of Immunology 204, no. 1_Supplement (May 1, 2020): 243.3. http://dx.doi.org/10.4049/jimmunol.204.supp.243.3.
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Abstract Multiplex IF (mIF) provides a detailed characterization of spatial relationships and complex cell phenotypes in the tumor microenvironment. However, the data-analysis and visualization is complex and time-consuming. Here, we developed a platform to analyze mIF data through flow cytometry workflows (image cytometry), while maintaining spatial information, and applied it to tissue microarrays of metastatic melanoma specimens (n=93; 6-plex mIF panel: PD-1, PD-L1, CD163, CD8, FoxP3, Sox10/S100). Then, we used a UMAP-based approach driven by cell-to-cell distances (rather than fluorescence intensity) to display and analyze geographic organization and cell interactions. Our pipeline provided equivalent results to the digital pathology gold standard with faster run times (5-fold reduction) and higher reproducibility. We identified key prognostic immune variables, including CD8PD1Low and CD8PD1Neg cells which associated with longer overall survival (OS, both p<0.01), and CD163PDL1Neg cells which associated with shorter OS (p=0.001). The spatial UMAPs showed that PD-L1 and PD-1 intensities were spatially encoded, and their expression on distinct cell subsets was organized in geographic clusters. Specifically, PD-L1Hi cells co-located to areas of CD8 cells, and PD-1Hi cells were observed near dense collections of tumor cells. Spatial UMAP subtraction analysis (survivors vs. non-survivors at 5 years) identified geographic and co-expression signatures associated with improved prognosis, i.e. CD8-driven PD-L1 expression and lacking CD163PDL1Neg macrophages. These data demonstrate the use of image cytometry and spatial UMAPs for improved visualization and interpretation of single-cell, spatially-resolved mIF data.
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Jemaa, Skander, Jill Fredrickson, Alexandre Coimbra, Richard AD Carano, Tarec Christoffer C. El-Galaly, Andrea Knapp, Tina G. Nielsen, Deniz Sahin, Thomas Bengtsson, and Alex de Crespigny. "A Fully Automated Measurement of Total Metabolic Tumor Burden in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma." Blood 134, Supplement_1 (November 13, 2019): 4666. http://dx.doi.org/10.1182/blood-2019-124793.
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Introduction: Baseline total metabolic tumor volume (TMTV) from FDG-PET/CT scans has been shown to be prognostic for progression-free survival (PFS) in diffuse large B-cell lymphoma (DLBCL; Kostakoglu et al. Blood 2017) and follicular lymphoma (FL; Meignan et al. J Clin Oncol 2016). Fully automated TMTV measurements could increase reproducibility and enable results in real-time after a PET/CT scan. Although numerous methods for tumor segmentation on FDG PET images are published, they typically involve a manual step to identify a point within each tumor, performed by a trained reader, followed by semi-automatic identification of the tumor margins. To allow for rapid segmentation of whole body metabolic tumor burden, we developed a fully automated approach based on deep learning algorithms. Methods: An image processing pipeline was developed using FDG-PET/CT images from two large Phase III, multicenter trials, in first-line (1L) DLBCL (GOYA, NCT01287741, n=1418) and FL (GALLIUM, NCT01332968, n=1401). FDG-PET/CT scans were acquired according to a standardized imaging charter using a range of scanner models. Images were automatically preprocessed and used as inputs to cascaded 2D and region-specific 3D convolutional neural networks. The resulting tumor masks were then used for feature extraction. For simplicity, our prognostic analysis is limited to three variables: TMTV, number of identified lesions, and bulky disease (longest tumor diameter >7.5cm). For tumor segmentation, neural networks were trained on 2,266 scans from 1,133 patients in GOYA, and tested (out-of-sample) on 1,064 scans from 532 patients with evaluable baseline and end-of-treatment scans in GALLIUM. Manually directed, semi-automated tumor masks reviewed by board certified radiologists were used as ground truth for both training and testing. Based on the extracted tumor information, prognostic analyses for PFS were conducted on 1,139 evaluable pretreatment PET/CT scans from GOYA, and 541 patients from GALLIUM. Kaplan-Meier methodology was used for survival analysis, and a Cox proportional hazards (CPH) model was used for multivariate analysis. Results: From the out-of-sample validation step, the Dice Similarity Coefficient for the segmented tumor burden was 0.886, while the voxelwise sensitivity was 0.926. The lesion-level correlation between extracted and measured TMTV was 0.987. For PFS in the 1L DLBCL trial (GOYA), our calculated patient-level TMTV quartiles closely replicate the prognostic results of the semi-automated analysis reported by Kostakoglu et al. (Fig 1A, Table 1). A high lesion count above Q3 (>12 lesions [Fig 1B]) and bulky disease were also prognostic for PFS. To evaluate the prognostic value of the derived metrics, a simple risk score (RS) was constructed by considering the quantity: RS-DLBCL = 𝟙(TMTV >330ml) + 𝟙(nr. lesions ≥12) + 𝟙(bulky disease >1), where 𝟙(.) denotes the indicator function and 330ml is the median TMTV in GOYA. Multivariate CPH analysis verified the unique contribution of RS-DLBCL (p<0.0005) when added to the International Prognostic Index (IPI) score (p<0.01); derived from the multivariate model, the estimated HRs for RS-DLBCL are given in Table 2. In the 1L FL trial (GALLIUM), baseline TMTV >510mL was prognostic for PFS (HR, 1.59; p<0.013; Fig 1C). A high lesion count above Q3 (>18 lesions) and bulky disease (Fig 1D) were also prognostic. Three-year PFS for patients with TMTV <510mL was 85.1% (81.3-89.1%), while for TMTV >510mL, it was 77.3% (71.3-83.7%). A RS for 1L FL was defined similarly as for DLBCL: RS-FL = 𝟙(TMTV >510ml) + 𝟙(nr. lesions >18) + 𝟙(bulky disease). RS-FL (p<0.034) was significant when added to a CPH model with FLIPI (p<0.024). Estimated HRs for RS-FL after adjusting for FLIPI are given in Table 2. Conclusion: We present a novel approach for a fully automated whole body metabolic tumor burden segmentation on FDG-PET/CT scans for non-Hodgkin lymphoma patients. This method allows for the extraction of a range of tumor burden features from FDG-PET/CT. For example, TMTV, number of lesions, and bulky disease-features shown to be prognostic for PFS-in addition to known factors such as IPI/FLIPI. Our method is fast and produces a complete pt-level assessment in <5mins. Further development including clinical and biomarker covariates, and considering organ involvement, may yield better prognostic performance to identify pts who are likely to progress within 1-2 years. Disclosures Jemaa: Genentech, Inc./F. Hoffmann-La Roche Ltd: Employment. Fredrickson:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. Coimbra:Genentech, Inc.: Employment. Carano:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. El-Galaly:Takeda: Other: Travel support; Roche: Employment, Other: Travel support. Knapp:F. Hoffmann-La Roche Ltd: Employment. Nielsen:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Sahin:F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Bengtsson:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership. de Crespigny:Genentech, Inc.: Employment; F. Hoffmann-La Roche Ltd: Equity Ownership.
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Mehra, Navi, Bhavika Patel, Stephanie Allen, Brittney Boldt, Noah Ramirez, Najiba Mammadova, and Agnes Haggerty. "Multispectral imaging to detect immune phenotypes associated with the tumor microenvironment in a multi-tissue study: A full automated 7-color mIF immuno-oncology workflow." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 48.15. http://dx.doi.org/10.4049/jimmunol.208.supp.48.15.
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Abstract Immunotherapy and precision medicine are rapidly developing approaches to cancer therapy. Biomarkers that detect the tumor and tumor microenvironment allow for the development of strategies that accelerate the advancement of treatments to enhance a patient’s immune system. Akoya’s MOTiF™ PD-1/PD-L1 Panel is a validated, multiplex immunoassay enabling detection of the 6 most clinically relevant immuno-oncology and spatial biomarkers: PD-1, PD-L1, FoxP3, CD8, CD68, and PanCK. The MOTiF™ PD-1/PD-L1 Panel was used to analyze the tumor microenvironment and specifically assess immune phenotypes of different types of cancers: non-small cell lung cancer (NSCLC), colon adenocarcinoma, head and neck squamous cell carcinoma (HNSCC), and breast cancer. We demonstrate the utility of Akoya’s MOTiF™ PD-1/PD-L1 panel kit in studying the cellular diversity of various cancers while retaining spatial context. Stained slides were analyzed using the InForm® and PhenoptrReports image analysis platforms to identify and better understand spatial relationships between a variety of simple and complex cell phenotypes. The MOTiF™ PD-1/PD-L1 panel kit provides reproducibility across different tissue types. These data provide insight into the innate and adaptive immune environment for targeted design of new immunotherapies and have implications for improving the treatment paradigm across many tumor types.
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Anderson, Ashley N., Jace B. King, and Jeffrey S. Anderson. "Neuroimaging in Psychiatry and Neurodevelopment: why the emperor has no clothes." British Journal of Radiology 92, no. 1101 (September 2019): 20180910. http://dx.doi.org/10.1259/bjr.20180910.
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Neuroimaging has been a dominant force in guiding research into psychiatric and neurodevelopmental disorders for decades, yet researchers have been unable to formulate sensitive or specific imaging tests for these conditions. The search for neuroimaging biomarkers has been constrained by limited reproducibility of imaging techniques, limited tools for evaluating neurochemistry, heterogeneity of patient populations not defined by brain-based phenotypes, limited exploration of temporal components of brain function, and relatively few studies evaluating developmental and longitudinal trajectories of brain function. Opportunities for development of clinically impactful imaging metrics include longer duration functional imaging data sets, new engineering approaches to mitigate suboptimal spatiotemporal resolution, improvements in image post-processing and analysis strategies, big data approaches combined with data sharing of multisite imaging samples, and new techniques that allow dynamical exploration of brain function across multiple timescales. Despite narrow clinical impact of neuroimaging methods, there is reason for optimism that imaging will contribute to diagnosis, prognosis, and treatment monitoring for psychiatric and neurodevelopmental disorders in the near future.
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Møller, Jakob Møllenbach, Mikkel Østergaard, Henrik S. Thomsen, Stine Hangaard, Inge J. Sørensen, Ole Rintek Madsen, and Susanne J. Pedersen. "Repeatability and reproducibility of MRI apparent diffusion coefficient applied on four different regions of interest for patients with axial spondyloarthritis and healthy volunteers scanned twice within a week." BJR|Open 2, no. 1 (November 2020): 20200004. http://dx.doi.org/10.1259/bjro.20200004.
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Objectives: The apparent diffusion coefficient (ADC) may be used as a biomarker for diagnosis and/or monitoring treatment response in patients with axial spondyloarthritis (axSpA), but this requires reliable ADC measurements. This study assessed test–retest repeatability and reproducibility of ADC measurements using four different region of interest (ROI) settings. Methods: In this prospective study, the sacroiliac joints (SIJs) of 25 patients with axSpA and 24 age- and sex-matched healthy volunteers were imaged twice at a mean interval of 6.8 days in a 1.5 T scanner using, multishot echoplanar diffusion-weighted sequences. ADCs at four ROI settings were assessed: 5 mm and 10 mm anatomic band-shaped, 15 mm linear, and 40 mm2 circular. Results: Intraclass correlation coefficient (ICC) assessments showed that the interstudy repeatability was good for median ADC (ADCmed) and 95th-percentile ADC (ADC95) measurements in patients with axSpA (0.77–0.83 and 0.75–0.83, respectively), but poor-to-moderate in healthy subjects (0.27–0.55 and 0.13–0.37, respectively). For all ROI settings, intrareader reproducibility was excellent for ADCmed-measurements (ICC:0.85–0.99) and moderate-to-excellent for ADC95 measurements (ICC:0.68–0.96). The 5 mm ROI had the least estimated bias and highest level of agreement on Bland–Altman plots. The interreader reproducibility was moderate (ICC:0.71). The 15 mm linear ROI produced significantly greater ADCmed and ADC95 measurements than all other ROI settings (p < 0.01–0.02), except for the circular ROI ADC95 measurements. Conclusion: ROI settings influence ADC measurements. Interstudy repeatability of SIJ ADC measurements is independent of ROI settings. However, the 5 mm ROI showed the least bias and random error and seems preferable. Advances in knowledge: ADC measurements are affected by ROI settings, and this should be taken into account when assessing ADC maps.
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Botta, Francesca, Mahila Ferrari, Sara Raimondi, Federica Corso, Giuliana Lo Presti, Saveria Mazzara, Lighea Simona Airò Farulla, et al. "The Impact of Segmentation Method and Target Lesion Selection on Radiomic Analysis of 18F-FDG PET Images in Diffuse Large B-Cell Lymphoma." Applied Sciences 12, no. 19 (September 27, 2022): 9678. http://dx.doi.org/10.3390/app12199678.
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Radiomic analysis of 18F[FDG] PET/CT images might identify predictive imaging biomarkers, however, the reproducibility of this quantitative approach might depend on the methodology adopted for image analysis. This retrospective study investigates the impact of PET segmentation method and the selection of different target lesions on the radiomic analysis of baseline 18F[FDG] PET/CT images in a population of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. The whole tumor burden was segmented on PET images applying six methods: (1) 2.5 standardized uptake value (SUV) threshold; (2) 25% maximum SUV (SUVmax) threshold; (3) 42% SUVmax threshold; (4) 1.3∙liver uptake threshold; (5) intersection among 1, 2, 4; and (6) intersection among 1, 3, 4. For each method, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG) were assessed, and their association with survival outcomes (progression-free survival PFS and overall survival OS) was investigated. Methods 1 and 2 provided stronger associations and were selected for the next steps. Radiomic analysis was then performed on two target lesions for each patient: the one with the highest SUV and the largest one. Fifty-three radiomic features were extracted, and radiomic scores to predict PFS and OS were obtained. Two proportional-hazard regression Cox models for PFS and OS were developed: (1) univariate radiomic models based on radiomic score; and (2) multivariable clinical–radiomic model including radiomic score and clinical/diagnostic parameters (IPI score, SUVmax, TMTV, WTLG, lesion volume). The models were created in the four scenarios obtained by varying the segmentation method and/or the target lesion; the models’ performances were compared (C-index). In all scenarios, the radiomic score was significantly associated with PFS and OS both at univariate and multivariable analysis (p < 0.001), in the latter case in association with the IPI score. When comparing the models’ performances in the four scenarios, the C-indexes agreed within the confidence interval. C-index ranges were 0.79–0.81 and 0.80–0.83 for PFS radiomic and clinical–radiomic models; 0.82–0.87 and 0.83–0.90 for OS radiomic and clinical–radiomic models. In conclusion, the selection of either between two PET segmentation methods and two target lesions for radiomic analysis did not significantly affect the performance of the prognostic models built on radiomic and clinical data of DLBCL patients. These results prompt further investigation of the proposed methodology on a validation dataset.
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Tzoras, Evangelos, Ioannis Zerdes, Nikos Tsiknakis, Georgios C. Manikis, Artur Mezheyeuski, Jonas Bergh, Alexios Matikas, and Theodoros Foukakis. "Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution." Cancers 14, no. 8 (April 14, 2022): 1999. http://dx.doi.org/10.3390/cancers14081999.
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The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.
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Patel, Bhavika, Stephanie Allen, Brittney Boldt, Najiba Mammadova, Agnes Haggerty, and Navi Mehra. "Abstract 2769: Advance understanding of the tumor microenvironment with multiplex immunofluorescence." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2769. http://dx.doi.org/10.1158/1538-7445.am2022-2769.
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Abstract Immunotherapy and precision medicine are rapidly developing approaches to cancer therapy. Biomarkers that detect the tumor and tumor microenvironment allow for the development of strategies that accelerate the advancement of treatments to enhance a patient’s immune system. Akoya’s MOTiF™ PD-1/PD-L1 Panel is a validated, multiplex immunoassay enabling detection of the 6 most clinically relevant immuno-oncology and spatial biomarkers: PD-1, PD-L1, FoxP3, CD8, CD68, and PanCK. The MOTiF™ PD-1/PD-L1 Panel was used to analyze the tumor microenvironment and specifically assess immune phenotypes of different types of cancers: non-small cell lung cancer (NSCLC), colon adenocarcinoma, head and neck squamous cell carcinoma (HNSCC), and breast cancer. We demonstrate the utility of Akoya’s MOTiF™ PD-1/PD-L1 panel kit in studying the cellular diversity of various cancers while retaining spatial context. Stained slides were analyzed using the InForm® and PhenoptrReports image analysis platforms to identify and better understand spatial relationships between a variety of simple and complex cell phenotypes. The MOTiF™ PD-1/PD-L1 panel kit provides reproducibility across different tissue types. These data provide insight into the innate and adaptive immune environment for targeted design of new immunotherapies and have implications for improving the treatment paradigm across many tumor types. Citation Format: Bhavika Patel, Stephanie Allen, Brittney Boldt, Najiba Mammadova, Agnes Haggerty, Navi Mehra. Advance understanding of the tumor microenvironment with multiplex immunofluorescence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2769.
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Khare, Sonal, Chi-Sing Ho, Madhavi Kannan, Brian Larsen, Brandon Mapes, Jenna Shaxted, Jagadish Venkataraman, and Ameen Salahudeen. "62 Applying machine vision to empower preclinical development of cell engager and adoptive cell therapeutics in patient-derived organoid models of solid tumors." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A70. http://dx.doi.org/10.1136/jitc-2021-sitc2021.062.
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BackgroundCell engager and adoptive cell therapeutics have emerged as efficacious and durable treatments in patients with B-cell malignancies. Though many analogous strategies are under development in solid tumors, none have received approval. Preclinical development of these therapies requires cell labeling of immortalized cell lines and/or primary expanded T cells to distinguish target and effector cells. However, cell engager and adoptive cell therapies have had limited evidence of reproducibility in primary patient-derived models such as tumor organoid cultures thus far. Here, we build upon our tumor organoid platform1 to measure organoid specific responses to these therapies. Utilizing machine vision coupled with time-lapse-microscopy, we obtain multiparameter kinetic readouts of patient-derived tumor organoid cell killing and allogeneic MHC-matched primary peripheral blood mononuclear cells (PBMCs).MethodsThe patient-derived tumor organoids were co-cultured with PBMCs in the presence of engagers/activators and vital dyes and incubated for 96 hrs. Cell death was measured by quantifying the caspase 3/7 vital dye pixel intensities at different time points using high throughput imaging. As a first step, a fully convolutional neural network was trained to segment out organoids from brightfield images comprised of organoids, immune cells and potential background artifacts. This segmentation mask was then transferred over to registered caspase 3/7 images to quantify tumor cell specific phenotypes in a rapid and automated manner.ResultsThe time-lapse imaging assay allowed for both the tracking of the organoid growth over time as well as the quantification of the kinetics of engagers/activators in comparison to controls resulting in accurate and precise technical reproducibility. Further, this assay allowed for the co-localization of the organoids and the immune cells over time, thus, enabling a spatiotemporal summary of dose dependent efficacy of candidate therapeutics.ConclusionsWe demonstrate the scalability and throughput of a machine vision tumor organoid immune co-culture platform across multiple unique patient-derived tumor organoid lines bearing a target of interest, enabling future discovery of biomarkers of therapeutic response and resistance.ReferenceLarsen B, Kannan M, Langer LF, Khan AA, Salahudeen AA, A pan-cancer organoid platform for precision medicine. Cell Reports 2021; 36:109429
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Hernando, Diego, Ruiyang Zhao, Qing Yuan, Mounes Aliyari Ghasabeh, Stefan Ruschke, Xinran Miao, Dimitrios C. Karampinos, et al. "Multi-Center, Multi-Vendor Reproducibility and Calibration of MRI-Based R2* for Liver Iron Quantification." Blood 138, Supplement 1 (November 5, 2021): 2010. http://dx.doi.org/10.1182/blood-2021-148803.
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Abstract Introduction: Excessive accumulation of iron is caused by a variety of conditions, including hereditary hemochromatosis and transfusional hemosiderosis. If untreated, iron overload can lead to damage in those organs where iron accumulates. Therefore, accurate and reproducible evaluation of body iron stores is needed to guide diagnosis, grading, and treatment monitoring of iron overload. While serum ferritin is the simplest means to assess body iron, it is also an acute phase reactant and therefore is not a reliable biomarker of body iron. Liver iron concentration (LIC) is directly and linearly related to total body iron stores. As such, LIC is widely recognized as a useful surrogate biomarker for the evaluation of iron overload. Liver biopsy is limited by its invasive nature and is contraindicated in many patients (eg. thrombocytopenia) due to bleeding risk. Magnetic resonance imaging (MRI) is a standard of care tool to measure LIC. Arguably the most practical method is R2* MRI due to its speed and ease of use, but the cross-vendor reproducibility of R2*-based LIC estimation remains unknown. Therefore, we evaluated the reproducibility and calibration of R2*-based LIC measurement via a single-breath-hold, confounder-corrected R2*-MRI at both 1.5T and 3T, through a multi-center, multi-vendor study. Methods: Four centers (University of Wisconsin-Madison, University of Texas-Southwestern, Johns Hopkins University, and Stanford University) using MRI scanners of different vendors (GE, Philips, and Siemens) participated in this HIPAA-compliant IRB-approved prospective cross-sectional study. This study recruited subjects with known or suspected iron overload from a variety of etiologies, including hereditary hemochromatosis, transfusional hemosiderosis (due to non-malignant or malignant conditions), and chronic liver disease. Subjects with were recruited for same day multiecho gradient-echo MRI for R2* mapping at both 1.5T and 3T (UW, UTSW, Stanford: 3.0T; JHU: 2.89T). R2* maps were reconstructed from the raw multiecho images and analyzed at a single center. Spin-echo MRI were also performed at 1.5T according to a standardized protocol (FerriScan, Resonance Health, Australia) and processed by a commercial algorithm to obtain FDA-approved reference standard LIC estimates. R2*-vs.-LIC calibrations were generated across centers and field strengths using linear regression and compared using F-tests. A predicted 2.89T calibration was interpolated from the 1.5T and 3.0T calibrations, and compared to the measured (JHU) calibration. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic accuracy of R2* MRI for detection of clinically relevant LIC thresholds. Results: A total of 200 subjects were recruited and successfully scanned for this study. We confirmed a linear relationship between R2* and LIC. All calibrations within the same field strength (see Figure 1) were highly reproducible showing no statistically significant center-specific differences (F > 3.0461). Pooled calibrations for 1.5T, 2.89T, and 3.0T were generated. At either field strength and for each of the LIC thresholds under consideration (1.8, 3.2, 7.0, 15.0 mg/g), estimated areas under the ROC curve (AUCs) of 0.98 or higher were observed. Discussion and Conclusions: In conclusion, confounder-corrected R2* MRI enables accurate and reproducible quantification of liver iron overload, over clinically relevant ranges of LIC. The data generated in this study provide the necessary calibrations for broad dissemination of R2*-based LIC quantification. Figure 1 Figure 1. Disclosures Hernando: Calimetrix: Current holder of individual stocks in a privately-held company. Pedrosa: Merck: Honoraria; Bayer Healthcare: Honoraria; Health Tech International: Current holder of stock options in a privately-held company. Vasanawala: HeartVista: Current holder of individual stocks in a privately-held company; InkSpace: Current holder of individual stocks in a privately-held company; Arterys: Current holder of individual stocks in a privately-held company. Reeder: Bayer: Research Funding; Pfizer: Research Funding; Calimetrix, LLC: Current holder of individual stocks in a privately-held company; Reveal Pharmaceuticals: Current holder of individual stocks in a privately-held company; Elucent Medical: Current holder of individual stocks in a privately-held company; Cellectar Biosciences: Current holder of individual stocks in a privately-held company; HeartVista: Current holder of individual stocks in a privately-held company.