Academic literature on the topic 'Image biomarker reproducibility'
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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.
Dissertations / Theses on the topic "Image biomarker reproducibility"
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Constantino, Cláudia Santos. "Reproducibility Study of Tumor Biomarkers Extracted from Positron Emission To-mography Images with 18F-Fluorodeoxyglucose." Master's thesis, 2019. http://hdl.handle.net/10362/91156.
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Introduction and aim Cancer is one of the main causes of death worldwide. Tumor diagnosis, staging, surveillance, prognosis and access to the response to therapy are critical when it comes to plan and analyze the optimal treatment strategies of cancer diseases. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging has provided some reliable prognostic factors in several cancer types, by extracting quantitative measures from the images obtained in clinics. The recent addition of digital equipment to the clinical armamentarium of PET leads to some concerns regarding inter-device data variability. Consequently, the reproducibility assess-ment of the tumor features, usually used in clinics and research, extracted from images acquired in an analog and new digital PET equipment is of paramount importance for use of multi-scanner studies in longitudinal patient’s studies. The aim of this study was to evaluate the inter-equipment reliability of a set of 25 lesional features commonly used in clinics and research.
Material and methods In order to access the features agreement, a dual imaging protocol was designed. Whole-body 18F-FDG PET images from 53 oncological patients were acquired, after a single 18F-FDG injection, with two devices alternatively: Philips Vereos Digital PET/CT (VE-REOS with three different reconstruction protocols- digital) and Philips GEMINI TF-16 (GEM-INI with single standard reconstruction protocol- analog). A nuclear medicine physician identi-fied 283 18F-FDG avid lesions. Then, all lesions (both equipment) were automatically segmented based on a Bayesian classifier optimized to this study. In the total, 25 features (first order statistics and geometric features) were computed and compared. The intraclass correlation coefficient (ICC) was used as measure of agreement.
Results A high agreement (ICC > 0.75) was obtained for most of the lesion features pulled out from both devices imaging data, for all (GEMINI vs VEREOS) reconstructions. The lesion fea-tures most frequently used, maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis reached maximum ICC of 0.90, 0.98 and 0.97, respectively.
Conclusions Under controlled acquisition and reconstruction parameters, most of the features studied can be used for research and clinical work, whenever multiple scanner (e.g. VEREOS and GEMINI) studies, mainly during longitudinal patient evaluation, are used.
Book chapters on the topic "Image biomarker reproducibility"
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Starke, Ludger, Karsten Tabelow, Thoralf Niendorf, and Andreas Pohlmann. "Denoising for Improved Parametric MRI of the Kidney: Protocol for Nonlocal Means Filtering." In Methods in Molecular Biology, 565–76. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_34.
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AbstractIn order to tackle the challenges caused by the variability in estimated MRI parameters (e.g., T2* and T2) due to low SNR a number of strategies can be followed. One approach is postprocessing of the acquired data with a filter. The basic idea is that MR images possess a local spatial structure that is characterized by equal, or at least similar, noise-free signal values in vicinities of a location. Then, local averaging of the signal reduces the noise component of the signal. In contrast, nonlocal means filtering defines the weights for averaging not only within the local vicinity, bur it compares the image intensities between all voxels to define “nonlocal” weights. Furthermore, it generally compares not only single-voxel intensities but small spatial patches of the data to better account for extended similar patterns. Here we describe how to use an open source NLM filter tool to denoise 2D MR image series of the kidney used for parametric mapping of the relaxation times T2* and T2.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.
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Meyer, Sandra, Dieter Fuchs, and Martin Meier. "Ultrasound and Photoacoustic Imaging of the Kidney: Basic Concepts and Protocols." In Methods in Molecular Biology, 109–30. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_7.
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AbstractNoninvasive, robust, and reproducible methods to image kidneys are provided by different imaging modalities. A combination of modalities (multimodality) can give better insight into structure and function and to understand the physiology of the kidney. Magnetic resonance imaging can be complemented by a multimodal imaging approach to obtain additional information or include interventional procedures. In the clinic, renal ultrasound has been essential for the diagnosis and management of kidney disease and for the guidance of invasive procedures for a long time. Adapting ultrasound to preclinical requirements and for translational research, the combination with photoacoustic imaging expands the capabilities to obtain anatomical, functional, and molecular information from animal models. This chapter describes the basic concepts of how to image kidneys using different and most appropriate modalities.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.
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Grist, James T., Esben Søvsø Szocska Hansen, Frank G. Zöllner, and Christoffer Laustsen. "Analysis Protocol for Renal Sodium (23Na) MR Imaging." In Methods in Molecular Biology, 689–96. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_41.
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AbstractThe signal acquired in sodium (23Na) MR imaging is proportional to the concentration of sodium in a voxel, and it is possible to convert between the two using external calibration phantoms. Postprocessing, and subsequent analysis, of sodium renal images is a simple task that can be performed with readily available software. Here we describe the process of conversion between sodium signal and concentration, estimation of the corticomedullary sodium gradient and the procedure used for quadrupolar relaxation analysis.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.
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Riazy, Leili, Bastien Milani, João S. Periquito, Kathleen Cantow, Thoralf Niendorf, Menno Pruijm, Erdmann Seeliger, and Andreas Pohlmann. "Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects." In Methods in Molecular Biology, 549–64. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_33.
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AbstractFunctional renal MRI promises access to a wide range of physiologically relevant parameters such as blood oxygenation, perfusion, tissue microstructure, pH, and sodium concentration. For quantitative comparison of results, representative values must be extracted from the parametric maps obtained with these different MRI techniques. To improve reproducibility of results this should be done based on regions-of-interest (ROIs) that are clearly and objectively defined.Semiautomated subsegmentation of the kidney in magnetic resonance images represents a simple but very valuable approach for the quantitative analysis of imaging parameters in multiple ROIs that are associated with specific anatomic locations. Thereby, it facilitates comparing MR parameters between different kidney regions, as well as tracking changes over time.Here we provide detailed step-by-step instructions for two recently developed subsegmentation techniques that are suitable for kidneys of small rodents: i) the placement of ROIs in cortex, outer and the inner medulla based on typical kidney morphology and ii) the division of the kidney into concentrically oriented layers.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.
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Ramos Delgado, Paula, Ekkehard Küstermann, André Kühne, Jason M. Millward, Thoralf Niendorf, Andreas Pohlmann, and Martin Meier. "Hardware Considerations for Preclinical Magnetic Resonance of the Kidney." In Methods in Molecular Biology, 131–55. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_8.
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AbstractMagnetic resonance imaging (MRI) is a noninvasive imaging technology that offers unparalleled anatomical and functional detail, along with diagnostic sensitivity. MRI is suitable for longitudinal studies due to the lack of exposure to ionizing radiation. Before undertaking preclinical MRI investigations of the kidney, the appropriate MRI hardware should be carefully chosen to balance the competing demands of image quality, spatial resolution, and imaging speed, tailored to the specific scientific objectives of the investigation. Here we describe the equipment needed to perform renal MRI in rodents, with the aim to guide the appropriate hardware selection to meet the needs of renal MRI applications.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This chapter on hardware considerations for renal MRI in small animals is complemented by two separate publications describing the experimental procedure and data analysis.
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Grist, James T., Esben Søvsø Hansen, Frank G. Zöllner, and Christoffer Laustsen. "Sodium (23Na) MRI of the Kidney: Basic Concept." In Methods in Molecular Biology, 257–66. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_15.
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AbstractThe handling of sodium by the renal system is a key indicator of renal function. Alterations in the corticomedullary distribution of sodium are considered important indicators of pathology in renal diseases. The derangement of sodium handling can be noninvasively imaged using sodium magnetic resonance imaging (23Na MRI), with data analysis allowing for the assessment of the corticomedullary sodium gradient. Here we introduce sodium imaging, describe the existing methods, and give an overview of preclinical sodium imaging applications to illustrate the utility and applicability of this technique for measuring renal sodium handling.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.
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Jerome, Neil Peter, and João S. Periquito. "Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models." In Methods in Molecular Biology, 611–35. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_37.
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AbstractAnalysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.
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Waiczies, Sonia, Christian Prinz, Ludger Starke, Jason M. Millward, Paula Ramos Delgado, Jens Rosenberg, Marc Nazaré, Helmar Waiczies, Andreas Pohlmann, and Thoralf Niendorf. "Functional Imaging Using Fluorine (19F) MR Methods: Basic Concepts." In Methods in Molecular Biology, 279–99. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_17.
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AbstractKidney-associated pathologies would greatly benefit from noninvasive and robust methods that can objectively quantify changes in renal function. In the past years there has been a growing incentive to develop new applications for fluorine (19F) MRI in biomedical research to study functional changes during disease states. 19F MRI represents an instrumental tool for the quantification of exogenous 19F substances in vivo. One of the major benefits of 19F MRI is that fluorine in its organic form is absent in eukaryotic cells. Therefore, the introduction of exogenous 19F signals in vivo will yield background-free images, thus providing highly selective detection with absolute specificity in vivo. Here we introduce the concept of 19F MRI, describe existing challenges, especially those pertaining to signal sensitivity, and give an overview of preclinical applications to illustrate the utility and applicability of this technique for measuring renal function in animal models.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.