Journal articles on the topic 'Imaging biomarker validation'
To see the other types of publications on this topic, follow the link: Imaging biomarker validation.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Imaging biomarker validation.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Kammer, Michael N., Stephen A. Deppen, Sanja Antic, S. M. Jamshedur Rahman, Rosana Eisenberg, Fabien Maldonado, Melinda C. Aldrich, et al. "The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies." Cancer Biomarkers 33, no. 4 (April 18, 2022): 449–65. http://dx.doi.org/10.3233/cbm-210382.
Full textAbstract:
The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.
2
Kammer, Michael N., Stephen A. Deppen, Sanja Antic, S. M. Jamshedur Rahman, Rosana Eisenberg, Fabien Maldonado, Melinda C. Aldrich, et al. "The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies." Cancer Biomarkers 33, no. 4 (April 18, 2022): 449–65. http://dx.doi.org/10.3233/cbm-210382.
Full textAbstract:
The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.
3
De Jesus, J. R., and Marco Arruda. "Human disease biomarkers: challenges, advances, and trends in their validation." Journal of Integrated OMICS 11, no. 2 (December 29, 2021): 16–28. http://dx.doi.org/10.5584/jiomics.v11i2.207.
Full textAbstract:
Biomarkers are important tools in the medical field, once they allow better prediction, characterization, and treatment of diseases. In this scenario, it is essential that biomarkers are highly accurate. Thus, biomarker validation is an essential part of ensuring the effectiveness of a biomarker. Validation of biomarkers is the process by which biomarkers are evaluated for accuracy and consistency, as well as their ability to inform the condition of health or disease. Although, there is no unique measure that can be used to determine the validity for all biomarkers, there are general criteria that all biomarkers must meet to be useful. In this work, we review the definition of biomarkers and discuss the validity components. We then critically discuss the main methods used to validate biomarkers and consider some examples of biomarkers of the diseases which most killer in the world (cardiovascular diseases, cancer, and viral infections), highlighting the potential biochemical pathways of these biomarkers in the biological system. In addition, we also comment on the omic strategies used in the biomarker discovery process and conclude with information about perspectives in biomarker validation through imaging techniques.
4
Schwamborn, Kristina. "Imaging mass spectrometry in biomarker discovery and validation." Journal of Proteomics 75, no. 16 (August 2012): 4990–98. http://dx.doi.org/10.1016/j.jprot.2012.06.015.
Full text
5
Ahmed, Aqsa, Waleed AL-Ansi, Samra Basharat, Ye Li, and Zhonghu Bai. "Validation of Protein Biomarker Candidates for Diagnosis of HBV induced HCC." International Journal of Advances in Agricultural Science and Technology 9, no. 3 (March 30, 2022): 9–42. http://dx.doi.org/10.47856/ijaast.2022.v09i03.002.
Full textAbstract:
Hepatocellular carcinoma is a major contributor to the global cancer burden. It affects millions of people in Pakistan on a yearly basis. Furthermore, HCC is linked to viral infections Hepatitis B and C, which account for roughly 87 percent of HCC cases in Pakistan. HCC is identified using imaging techniques such as MRI, Ultrasound, and histology, which have radiation hazards and frequently need expensive healthcare systems that are less available in most of the developing countries. Novel HCC biomarkers are being developed as part of a large research project aimed at detecting the disease early. These include the creation of biomarkers based on HCC patients' transcriptome and proteomic profiles. Circulating proteins, which are easily detected in body fluids, including blood serum, may thus provide an opportunity for the development of HCC biomarkers. Blood-based serum biomarkers must be developed for easy, non-invasive, and early detection of HCC. In conjunction with imaging techniques, alpha-fetoprotein (AFP) has been used to detect HCC, although it has little clinical usefulness. Also, the reported AFP negative results make its utility meager. Multiple circulating proteins have been studied as biomarker possibilities for HCC diagnosis in recent years. In this study, Blood serum was used to validate three novel protein biomarker candidates to detect HBV induced HCC that had previously been predicted using a bioinformatics methodology. Proteins named C6, C8A and C8B were measured in the serum of 22 HCC patients infected with HBV in Pakistani population and compared to AFP levels using quantitative ELISA. C8A possesses considerable biomarker potential, with 95.45 percent specificity and 77.27% sensitivity with 0.933 Area Under the Curve (AUC), whereas C6 and C8B showed poor biomarker potential. Hence, C8A demonstrated great promise as a circulating blood-based protein biomarker for HBV induced HCC diagnosis.
6
D’Agostino, Maria-Antonietta, Maarten Boers, John Kirwan, Désirée van der Heijde, Mikkel Østergaard, Georg Schett, Robert B. Landewé, et al. "Updating the OMERACT Filter: Implications for Imaging and Soluble Biomarkers." Journal of Rheumatology 41, no. 5 (March 1, 2014): 1016–24. http://dx.doi.org/10.3899/jrheum.131313.
Full textAbstract:
Objective.The Outcome Measures in Rheumatology (OMERACT) Filter provides a framework for the validation of outcome measures for use in rheumatology clinical research. However, imaging and biochemical measures may face additional validation challenges because of their technical nature. The Imaging and Soluble Biomarker Session at OMERACT 11 aimed to provide a guide for the iterative development of an imaging or biochemical measurement instrument so it can be used in therapeutic assessment.Methods.A hierarchical structure was proposed, reflecting 3 dimensions needed for validating an imaging or biochemical measurement instrument: outcome domain(s), study setting, and performance of the instrument. Movement along the axes in any dimension reflects increasing validation. For a given test instrument, the 3-axis structure assesses the extent to which the instrument is a validated measure for the chosen domain, whether it assesses a patient-centered or disease-centered variable, and whether its technical performance is adequate in the context of its application. Some currently used imaging and soluble biomarkers for rheumatoid arthritis, spondyloarthritis, and knee osteoarthritis were then evaluated using the original OMERACT Filter and the newly proposed structure. Breakout groups critically reviewed the extent to which the candidate biomarkers complied with the proposed stepwise approach, as a way of examining the utility of the proposed 3-dimensional structure.Results.Although there was a broad acceptance of the value of the proposed structure in general, some areas for improvement were suggested including clarification of criteria for achieving a certain level of validation and how to deal with extension of the structure to areas beyond clinical trials.Conclusion.General support was obtained for a proposed tri-axis structure to assess validation of imaging and soluble biomarkers; nevertheless, additional work is required to better evaluate its place within the OMERACT Filter 2.0.
7
Palermo, Giovanni, Sara Giannoni, Gabriele Bellini, Gabriele Siciliano, and Roberto Ceravolo. "Dopamine Transporter Imaging, Current Status of a Potential Biomarker: A Comprehensive Review." International Journal of Molecular Sciences 22, no. 20 (October 18, 2021): 11234. http://dx.doi.org/10.3390/ijms222011234.
Full textAbstract:
A major goal of current clinical research in Parkinson’s disease (PD) is the validation and standardization of biomarkers enabling early diagnosis, predicting outcomes, understanding PD pathophysiology, and demonstrating target engagement in clinical trials. Molecular imaging with specific dopamine-related tracers offers a practical indirect imaging biomarker of PD, serving as a powerful tool to assess the status of presynaptic nigrostriatal terminals. In this review we provide an update on the dopamine transporter (DAT) imaging in PD and translate recent findings to potentially valuable clinical practice applications. The role of DAT imaging as diagnostic, preclinical and predictive biomarker is discussed, especially in view of recent evidence questioning the incontrovertible correlation between striatal DAT binding and nigral cell or axon counts.
8
O’Rourke, Matthew B., Ben R. Roediger, Christopher J. Jolly, Ben Crossett, Matthew P. Padula, and Phillip M. Hansbro. "Viral Biomarker Detection and Validation Using MALDI Mass Spectrometry Imaging (MSI)." Proteomes 10, no. 3 (September 13, 2022): 33. http://dx.doi.org/10.3390/proteomes10030033.
Full textAbstract:
(1) Background: MALDI imaging is a technique that still largely depends on time of flight (TOF)-based instrument such as the Bruker UltrafleXtreme. While capable of performing targeted MS/MS, these instruments are unable to perform fragmentation while imaging a tissue section necessitating the reliance of MS1 values for peptide level identifications. With this premise in mind, we have developed a hybrid bioinformatic/image-based method for the identification and validation of viral biomarkers. (2) Methods: Formalin-Fixed Paraffin-Embedded (FFPE) mouse samples were sectioned, mounted and prepared for mass spectrometry imaging using our well-established methods. Peptide identification was achieved by first extracting confident images corresponding to theoretical viral peptides. Next, those masses were used to perform a Peptide Mmass Fingerprint (PMF) searched against known viral FASTA sequences against a background mouse FASTA database. Finally, a correlational analysis was performed with imaging data to confirm pixel-by-pixel colocalization and intensity of viral peptides. (3) Results: 14 viral peptides were successfully identified with significant PMF Scores and a correlational result of >0.79 confirming the presence of the virus and distinguishing it from the background mouse proteins. (4) Conclusions: this novel approach leverages the power of mass spectrometry imaging and provides confident identifications for viral proteins without requiring MS/MS using simple MALDI Time Of Flight/Time Of Flight (TOF/TOF) instrumentation.
9
Obuchowski, Nancy A., Erick M. Remer, Ken Sakaie, Erika Schneider, Robert J. Fox, Kunio Nakamura, Ricardo Avila, and Alexander Guimaraes. "Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects." Clinical Trials 18, no. 2 (January 10, 2021): 197–206. http://dx.doi.org/10.1177/1740774520981934.
Full textAbstract:
Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.
10
Weber, Wolfgang A. "Positron Emission Tomography As an Imaging Biomarker." Journal of Clinical Oncology 24, no. 20 (July 10, 2006): 3282–92. http://dx.doi.org/10.1200/jco.2006.06.6068.
Full textAbstract:
Positron emission tomography (PET) allows noninvasive, quantitative studies of various biologic processes in the tumor tissue. By using PET, investigators can study the pharmacokinetics of anticancer drugs, identify various therapeutic targets and monitor the inhibition of these targets during therapy. Furthermore, PET provides various markers to assess tumor response early in the course of therapy. A significant number of studies have now shown that changes in tumor glucose utilization during the first weeks of chemotherapy are significantly correlated with patient outcome. These data suggest that PET may be used as a sensitive test to assess the activity of new cytotoxic agents in phase II studies. Furthermore, early identification of nonresponding tumors provides the opportunity to adjust treatment regimens according to the individual chemosensitivity of the tumor tissue. However, further prospective and randomized validation of PET is still required before PET controlled chemotherapy can be used in clinical practice.
11
Hunter, D. J., F. Eckstein, V. B. Kraus, E. Losina, L. Sandell, and A. Guermazi. "Imaging Biomarker Validation and Qualification Report: Sixth OARSI Workshop on Imaging in Osteoarthritis combined with Third OA Biomarkers Workshop." Osteoarthritis and Cartilage 21, no. 7 (July 2013): 939–42. http://dx.doi.org/10.1016/j.joca.2013.04.014.
Full text
12
van Oostveen, Wieke M., and Elizabeth C. M. de Lange. "Imaging Techniques in Alzheimer’s Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring." International Journal of Molecular Sciences 22, no. 4 (February 20, 2021): 2110. http://dx.doi.org/10.3390/ijms22042110.
Full textAbstract:
Background. Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. Introduction. In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. Aim. In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. Findings. Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. Conclusions. Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.
13
Jentsch, C., B. Beuthien-Baumann, E. G. C. Troost, and G. Shakirin. "Validation of functional imaging as a biomarker for radiation treatment response." British Journal of Radiology 88, no. 1051 (July 2015): 20150014. http://dx.doi.org/10.1259/bjr.20150014.
Full text
14
Wynne, J. F., L. E. Colbert, C. Seldon, M. Zaid, D. S. Yu, J. C. Landry, and E. J. Koay. "External Validation of an Imaging-Based Biomarker of Pancreatic Ductal Adenocarcinoma." International Journal of Radiation Oncology*Biology*Physics 102, no. 3 (November 2018): e79. http://dx.doi.org/10.1016/j.ijrobp.2018.07.432.
Full text
15
Mankhong, Sakulrat, Sujin Kim, Seongju Lee, Hyo-Bum Kwak, Dong-Ho Park, Kyung-Lim Joa, and Ju-Hee Kang. "Development of Alzheimer’s Disease Biomarkers: From CSF- to Blood-Based Biomarkers." Biomedicines 10, no. 4 (April 5, 2022): 850. http://dx.doi.org/10.3390/biomedicines10040850.
Full textAbstract:
In the 115 years since the discovery of Alzheimer’s disease (AD), our knowledge, diagnosis, and therapeutics have significantly improved. Biomarkers are the primary tools for clinical research, diagnostics, and therapeutic monitoring in clinical trials. They provide much insightful information, and while they are not clinically used routinely, they help us to understand the mechanisms of this disease. This review charts the journey of AD biomarker discovery and development from cerebrospinal fluid (CSF) amyloid-beta 1-42 (Aβ42), total tau (T-tau), and phosphorylated tau (p-tau) biomarkers and imaging technologies to the next generation of biomarkers. We also discuss advanced high-sensitivity assay platforms for CSF Aβ42, T-tau, p-tau, and blood analysis. The recently proposed Aβ deposition/tau biomarker/neurodegeneration or neuronal injury (ATN) scheme might facilitate the definition of the biological status underpinning AD and offer a common language among researchers across biochemical biomarkers and imaging. Moreover, we highlight blood-based biomarkers for AD that offer a scalable alternative to CSF biomarkers through cost-saving and reduced invasiveness, and may provide an understanding of disease initiation and development. We discuss different groups of blood-based biomarker candidates, their advantages and limitations, and paths forward, from identification and analysis to clinical validation. The development of valid blood-based biomarkers may facilitate the implementation of future AD therapeutics and diagnostics.
16
Mai, Yingren, Qun Yu, Feiqi Zhu, Yishan Luo, Wang Liao, Lei Zhao, Chunyan Xu, et al. "AD Resemblance Atrophy Index as a Diagnostic Biomarker for Alzheimer’s Disease: A Retrospective Clinical and Biological Validation." Journal of Alzheimer's Disease 79, no. 3 (February 2, 2021): 1023–32. http://dx.doi.org/10.3233/jad-201033.
Full textAbstract:
Background: Magnetic resonance imaging (MRI) provides objective information about brain structural atrophy in patients with Alzheimer’s disease (AD). This multi-structural atrophic information, when integrated as a single differential index, has the potential to further elevate the accuracy of AD identification from normal control (NC) compared to the conventional structure volumetric index. Objective: We herein investigated the performance of such an MRI-derived AD index, AD-Resemblance Atrophy Index (AD-RAI), as a neuroimaging biomarker in clinical scenario. Method: Fifty AD patients (19 with the Amyloid, Tau, Neurodegeneration (ATN) results assessed in cerebrospinal fluid) and 50 age- and gender-matched NC (19 with ATN results assessed using positron emission tomography) were recruited in this study. MRI-based imaging biomarkers, i.e., AD-RAI, were quantified using AccuBrain®. The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of these MRI-based imaging biomarkers were evaluated with the diagnosis result according to clinical criteria for all subjects and ATN biological markers for the subgroup. Results: In the whole groups of AD and NC subjects, the accuracy of AD-RAI was 91%, sensitivity and specificity were 88% and 96%, respectively, and the AUC was 92%. In the subgroup of 19 AD and 19 NC with ATN results, AD-RAI results matched completely with ATN classification. AD-RAI outperforms the volume of any single brain structure measured. Conclusion: The finding supports the hypothesis that MRI-derived composite AD-RAI is a more accurate imaging biomarker than individual brain structure volumetry in the identification of AD from NC in the clinical scenario.
17
Jayson, Gordon C., Cong Zhou, Laura Hope Horsley, Kalena Marti, Danielle Shaw, Nerissa Mescallado, Andrew R. Clamp, et al. "Inter-tumor validation, through advanced MRI and circulating biomarkers, of plasma Tie2 as the vascular response biomarker for bevacizumab." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 11521. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11521.
Full textAbstract:
11521 Background: VEGF inhibitor (VEGFi) use is compromised by lack of predictive/ response biomarkers. Previously, we identified plasma Tie2 (pTie2) as a vascular response biomarker (VRB) for bevacizumab (bev) in ovarian cancer (OC). Here, we applied dynamic contrast-enhanced MRI (DCE-MRI) and circulating biomarkers in colorectal cancer (CRC), to validate pTie2 as the first tumor VRB. Methods: Seventy patients were recruited, with untreated, mCRC and ≥1 lesion of 3-10cm diameter for DCE-MRI. Patients received bev 10mg/kg for 2 weeks to elicit a biomarker response and then FOLFOX6/bev until progressive disease (PD) Thirteen circulating and 6 imaging biomarkers were measured before and during treatment and at PD. Unsupervised correlation analysis identified bev-induced biomarker correlations. Biomarkers were evaluated by clustered parameter-time course studies to determine their epithelial or vascular origin. Clinical significance was determined by relating the biomarker data to tumor 3D volumetric change assessed by MRI and PFS. The emergent vascular biomarker signal was modelled with epithelial biomarkers to assess the independent contribution of the vascular compartment to PD. Results: Bev induced significant correlations between pTie2, Ang2 and Ktrans. Cluster analysis of Tie2 concentration-time course curves showed that pTie2 reflected tumor Ktransbut not CK18, an epithelial antigen, i.e. changes in pTie2 reflected tumor vascular biology Patients who had the greatest area under the pTie2-time curve had tumors with high Ktransand/or low pVEGFR2, pre-treatment. They also had the greatest reduction in tumor volume and longest PFS. Fusion of pTie2 and CK18 data significantly improved modelling of PD. Conclusions: Bev impacts tumor vasculature causing proportional changes in pTie2. Information from pTie2 adds clinical value to that derived from the epithelial compartment. Thus (i) pTie2 is the first vascular response biomarker for bev and probably all VEGFi and (ii) demonstration of separate vascular and epithelial compartments in ovarian and CRC validates the vascular compartment as a target. This work identifies the first assay that could optimise use of VEGFi. Clinical trial information: 2009-011377-33.
18
Modur, Vijay, Eric Hailman, and JC Barrett. "Evidence-Based Laboratory Medicine in Oncology Drug Development: From Biomarkers to Diagnostics." Clinical Chemistry 59, no. 1 (January 1, 2013): 102–9. http://dx.doi.org/10.1373/clinchem.2012.191072.
Full textAbstract:
BACKGROUND The promise of targeted therapies in molecularly defined subsets of cancer has led to a transformation of the process of drug development in oncology. To target cancer successfully and precisely requires high-quality translational data. Such data can be generated by the use of biomarkers that answer key questions in drug development. CONTENT Translational data for aiding in decision-making and driving cancer drug development can be generated by systematic assessments with biomarkers. Types of biomarkers that support decisions include: pharmacodynamic assessments for selecting the best compound or dosage; assessment of early tumor response with tissue biomarkers and imaging, mutation, and other assessment strategies for patient selection; and the use of markers of organ injury to detect toxicity and improve safety. Tactics used to generate biomarker data include fit-for-purpose assay validation and real-time biomarker assessments. Successfully translated and clinically informative biomarkers can mature into novel companion diagnostic tests that expand the practice of laboratory medicine. SUMMARY Systematic biomarker assessments are a key component of the clinical development of targeted therapies for cancer. The success of these biomarker assessments requires applying basic principles of laboratory medicine to generate the data required to make informed decisions. Successful biomarkers can transition into diagnostic tests that expand the laboratory medicine armamentarium.
19
Hutchinson, Elizabeth, Susan Osting, Paul Rutecki, and Thomas Sutula. "Diffusion Tensor Orientation as a Microstructural MRI Marker of Mossy Fiber Sprouting After TBI in Rats." Journal of Neuropathology & Experimental Neurology 81, no. 1 (December 3, 2021): 27–47. http://dx.doi.org/10.1093/jnen/nlab123.
Full textAbstract:
Abstract Diffusion tensor imaging (DTI) metrics are highly sensitive to microstructural brain alterations and are potentially useful imaging biomarkers for underlying neuropathologic changes after experimental and human traumatic brain injury (TBI). As potential imaging biomarkers require direct correlation with neuropathologic alterations for validation and interpretation, this study systematically examined neuropathologic abnormalities underlying alterations in DTI metrics in the hippocampus and cortex following controlled cortical impact (CCI) in rats. Ex vivo DTI metrics were directly compared with a comprehensive histologic battery for neurodegeneration, microgliosis, astrocytosis, and mossy fiber sprouting by Timm histochemistry at carefully matched locations immediately, 48 hours, and 4 weeks after injury. DTI abnormalities corresponded to spatially overlapping but temporally distinct neuropathologic alterations representing an aggregate measure of dynamic tissue damage and reorganization. Prominent DTI alterations of were observed for both the immediate and acute intervals after injury and associated with neurodegeneration and inflammation. In the chronic period, diffusion tensor orientation in the hilus of the dentate gyrus became prominently abnormal and was identified as a reliable structural biomarker for mossy fiber sprouting after CCI in rats, suggesting potential application as a biomarker to follow secondary progression in experimental and human TBI.
20
Zaid, Mohamed, Baishali Chaudhury, Gauri R. Varadhachary, Matthew H. G. Katz, Joseph M. Herman, Eric P. Tamm, and Eugene Jon Koay. "Discovery and validation of a quantitative, stromal-associated imaging biomarker of pancreatic ductal adenocarcinoma (PDAC)." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 228. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.228.
Full textAbstract:
228 Background: As pancreatic ductal adenocarcinoma (PDAC) remains highly lethal, biomarkers are needed to identify patients who may benefit from specific therapeutic strategies. We previously described a qualitative computed tomography (CT) based biomarker - delta classification, whereby high delta tumors showed lower stromal content, more aggressive biology and poorer outcomes, than their counterparts. Here, we describe a quantitative method to differentiate these patients and predict outcomes. Methods: We retrospectively identified 101 treatment naïve patients who underwent pancreatectomy as a discovery cohort and 90 patients who underwent preoperative gemcitabine-based chemoradiation for validation. All patients underwent a pre-therapy pancreatic protocol CT and were classified as high or low delta, as described before. We semi-automatically segmented the tumors, chose normal pancreatic (NP) tissue and abdominal fat as references, then measured relative enhancement values using Philips IntelliSpace8 multimodality tumor tracking. We then analyzed the arterial and portal-venous phases separately using ROC and cox proportional hazards. Results: Delta class significantly associated with normalized enhancement values (NEV) in the arterial phase referenced to NP (P<0.0001, AUC =90%). A cutoff of 0.72 was identified that also distinguished high and low delta groups in the validation cohort (P<0.0001). As a continuous variable, the NEV was associated with distant metastasis free survival (DMFS) and overall survival (OS) on uni and multivariate analyses, accounting for traditional survival covariates. Using cutoff of 0.72, patients with high NEV had longer median OS (39 and 35.9 months) compared to those with low NEV (17.5 and 17.6 months, P=<0.0001) in discovery and validation cohorts, respectively. Similarly, patients with high NEV had longer median DMFS (46.6 and 62.2 months) compared to those with low NEV (15.6 and 13.1 months, P=0.005) in discovery and validation cohorts, respectively. Conclusions: The NEV measurement on baseline CT scans may serve as a quantitative imaging biomarker that can objectively reflect tumor biology and provide prognostic insight.
21
Kim, Bokkyu, and Carolee Winstein. "Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review." Neurorehabilitation and Neural Repair 31, no. 1 (August 19, 2016): 3–24. http://dx.doi.org/10.1177/1545968316662708.
Full textAbstract:
Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: ( a) lack of cross-validation, ( b) lack of minimal clinically important difference (MCID) for motor outcomes, and ( c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.
22
Nam, Hyeonseob, Ki Hwan Kim, and Chan-Young Ock. "AI-based imaging biomarker in mammography for prediction of tumor invasiveness." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 1568. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.1568.
Full textAbstract:
1568 Background: The preoperative diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) can be upstaged in the final pathology, and this possibility is linked to the controversy over whether axillary staging is necessary in primary operation. In this study, we developed an artificial intelligence (AI)-powered Imaging Biomarker in Mammography (IBM) that can predict tumor invasiveness in preoperative mammography and evaluated its performance in an external validation cohort. Methods: A total of 151,764 exams of 4-view mammograms were collected from five institutions of three countries to develop the AI algorithm for breast cancer detection, where 31,776 were cancer exams. In previous studies, the performance of this breast cancer detection algorithm has already been evaluated, and in this study, we further developed the AI-powered IBM for predicting tumor invasiveness on top of the AI algorithm for breast cancer detection. To develop the AI-powered IBM for predicting invasiveness, final diagnosis information was collected for 8,251 cancer exams (472 DCIS, 388 ductal carcinoma in situ (DCIS-MI), and 7,391 invasive ductal carcinoma (IDC)), and 886 cancer exams (44 DCIS, 51 DCIS-MI, 791 IDC) were additionally collected for internal validation. The AI-powered IBM was developed via two stages of training – 1) training with diagnosis labels (cancer vs non-cancer), followed by 2) fine-tuning with invasiveness labels (DCIS, DCIS-MI, IDC). The AI-powered IBM also tested in an external validation cohort of 699 cancer exams (68 DCIS, 19 DCIS-MI, 612 IDC) and all the exams were confirmed by surgical biopsy. Results: The AI-powered IBM showed an area under the curve (AUC) values of 0.968 for breast cancer detection and it successfully distinguished IDC from DCIS and DCIS-MI with AUC values of 0.898 and 0.851, respectively (Table). In addition, the AUC value in terms of discriminating between DCIS and DCIS-MI was 0.752. When the AI-powered IBM was tested in the external cohort, it could detect breast cancer with the AUC of 0.952 and, its performance in terms of invasiveness prediction was similar that of the internal validation (IDC vs DCIS, 0.810; IDC vs DCIS-MI, 0.846), which supports the AI-powered IBM is applicable to the unseen mammography exam. Conclusions: The AI-powered IBM can distinguish IDC from DCIS and DCIS-MI in mammography. The results support that the AI-powered IBM can be used as a biomarker to help determine the surgical plan that includes whether or not to perform the axillary dissection.[Table: see text]
23
Albala, David, Vladimir Mouraviev, Kimberly M. Rieger-Christ, Travis B. Sullivan, Naveen Kella, Kevin B. Knopf, Hani H. Rashid, et al. "A live cell microfluidics device utilizing phenotypic biomarkers for prostate cancer." Journal of Clinical Oncology 34, no. 2_suppl (January 10, 2016): 338. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.338.
Full textAbstract:
338 Background: A novel tissue based biomarker panel is introduced to objectively assess disease aggressiveness and invasive potential of Prostate Cancer (CaP). The biomarker diagnostic platform incorporates both molecular and phenotypic data that may allow an improved understanding of local growth and metastatic potential. The tissue based diagnostic incorporates matrix biology, phenotypic biomarkers, microfluidics, and machine vision. This technology presents the opportunity to culture samples, and both determine and automate biomarker measurements from machine vision algorithm analysis. Data are presented towards clinical validation, the ability to risk stratify, and prediction of local aggressiveness and metastasis. Methods: Conditions were optimized for reliably culturing primary cancer cells in vitro by simulating in vivo conditions on an extracellular matrix formulation. A mcirofluidics device was used to culture live tumor samples ex vivo enabling automated imaging of the label free and label-based biomarkers. Results: The validation study was IRB approved and performed in 200 consecutive CaP radical prostatectomy derived specimens collected between 03/2014 and 09/2015. Data was analyzed with receiver operating characteristics (ROC) generated Area-under-the-Curve (AUC) and specifically included capsular penetration, seminal vesicle invasion, as well as margin-positive disease. AUC Graphs are presented. The study further demonstrated that a normal set of phenotypic biomarkers can produce secondary metrics termed oncogenic potential (OP) and metastatic potential (MP). Concordance analysis supports that OP and MP are integral for distinguishing between benign histology and malignancy, predicting both stage and adverse pathology such as extra-prostatic extension (EPE) and lympho-vascular invasion (LVI). The study results demonstrate AUCs greater than 0.90 in predicting EPE and LVI. Conclusions: Results support the clinical validation of a novel live- cell phenotypic in vitro tumor diagnostic test. This test has the potential to predict adverse pathologies for CaP and may have extended clinical applications to optimize staging and risk stratification.
24
Kehl, Kenneth L., Michael J. Hassett, Katherine A. Stafford, Wenxin Xu, Bruce E. Johnson, and Deborah Schrag. "Development and validation of a novel EHR-based tumor progression outcome to support biomarker discovery." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e19297-e19297. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e19297.
Full textAbstract:
e19297 Background: Obtaining clinical outcomes for analysis has historically been a critical barrier to cancer genomics research. EHRs could constitute an important data source to bridge this gap, but EHRs rarely capture structured outcomes such as cancer progression. Novel, robust methods are needed to capture clinically relevant outcomes from EHRs. Methods: Among patients with lung adenocarcinoma whose tumors were sequenced via the Dana Farber Cancer Institute/Brigham and Women’s PROFILE study from 2013-2018, imaging reports following first palliative-intent systemic therapy were annotated using natural language processing (NLP) models trained to capture cancer progression according to the structured “PRISSMM” framework. NLP-based cancer progression and imaging report frequency were jointly modeled using inverse-intensity weighted generalized estimated equations, censored at six months, to explore associations between alterations in lung cancer biomarkers (ALK, EGFR, ROS1, BRAF, KRAS, SMARCA4) and progression. Among patients with KRAS mutations who received immunotherapy, we also analyzed the association between STK11 mutations and progression. The novel outcome generated by the model – imaging report-based progression (iPROG) – corresponded to the difference in the mean log odds of progression per inverse-intensity weighted report associated with a given biomarker; it was reported as adjusted mean probability and in exponentiated form as an odds ratio (OR). Results: Among 690 patients with lung adenocarcinoma, associations between tumor mutations and the iPROG outcome are listed in the Table. Conclusions: A deep NLP model applied to EHR data can capture a novel cancer progression outcome, which is associated with known prognostic markers in lung cancer. Application of this method to large “real world” datasets, with attention to interactions between treatment and genomics, could speed biomarker discovery. [Table: see text]
25
Quan, Kai, Jianping Song, Zixiao Yang, Dongdong Wang, Qingzhu An, Lei Huang, Peixi Liu, et al. "Validation of Wall Enhancement as a New Imaging Biomarker of Unruptured Cerebral Aneurysm." Stroke 50, no. 6 (June 2019): 1570–73. http://dx.doi.org/10.1161/strokeaha.118.024195.
Full text
26
Watcharatanyatip, Kamolwan, Somchai Chutipongtanate, Daranee Chokchaichamnankit, Churat Weeraphan, Kanokwan Mingkwan, Virat Luevisadpibul, David S. Newburg, Ardythe L. Morrow, Jisnuson Svasti, and Chantragan Srisomsap. "Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study." Molecules 27, no. 18 (September 11, 2022): 5904. http://dx.doi.org/10.3390/molecules27185904.
Full textAbstract:
Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study.
27
Wei, F., K. Diedrich, H. Fullerton, G. DeVeber, M. Wintermark, J. Hodge, and A. Kirton. "Arterial tortuosity: an imaging biomarker of childhood stroke pathogenesis?" Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 42, S1 (May 2015): S16. http://dx.doi.org/10.1017/cjn.2015.93.
Full textAbstract:
Background: Arteriopathy causes most childhood arterial ischemic stroke (AIS). Mechanisms are poorly understood but may include abnormalities of arterial structure. Extracranial dissection is common while intracranial dissection may explain idiopathic focal cerebral arteriopathy (FCA). We aimed to quantify cerebral arterial tortuosity and hypothesized increased tortuosity in extracranial dissection. Methods: Children with AIS were recruited within the Vascular-Effects-of-Infection-in-Pediatric-Stroke (VIPS) study (controls from the Calgary Pediatric Stroke Program). A validated software method calculated mean tortuosity of major cerebral arteries using 3D time-of-flight MR angiography (MRA). Blinded, multi-investigator reviews defined diagnostic categories. Tortuosity was compared between dissection (spontaneous and traumatic), FCA, moyamoya, meningitis, and cardioembolic, and controls (ANOVA, post-hoc Tukey). Results: A total of 116 children were studied. Age and gender were comparable across groups. Tortuosity scores and variances were consistent with validation studies. Tortuosity in controls (1.333±0.039, n=15) was comparable to moyamoya (1.324±0.038, p=0.99, n=15), meningitis (1.348±0.052, p=0.98, n=12) and cardioembolic (1.379±0.056, p=0.19, n=27) cases. Tortuosity was higher in dissection (1.398±0.072, p=0.02, n=22) and FCA (1.421±0.076, p=0.001, n=25). Traumatic (1.391±0.036, n=9) and non-traumatic (1.403±0.090, p=0.671, n=13) scores were not different. Conclusion: Children with dissection have more tortuous arteries. Quantified tortuosity may represent a clinically relevant biomarker of vascular biology in pediatric stroke.
28
Gallardo-Estrella, Leticia, Esther Pompe, Pim A. de Jong, Colin Jacobs, Eva M. van Rikxoort, Mathias Prokop, Clara I. Sánchez, and Bram van Ginneken. "Normalized emphysema scores on low dose CT: Validation as an imaging biomarker for mortality." PLOS ONE 12, no. 12 (December 11, 2017): e0188902. http://dx.doi.org/10.1371/journal.pone.0188902.
Full text
29
Kinahan, P. "TU-C-201C-02: Physical Validation of PET/CT for Imaging as a Biomarker." Medical Physics 37, no. 6Part8 (June 2010): 3387. http://dx.doi.org/10.1118/1.3469237.
Full text
30
Goodkin, Olivia, Hugh Pemberton, Sjoerd B. Vos, Ferran Prados, Carole H. Sudre, James Moggridge, M. Jorge Cardoso, et al. "The quantitative neuroradiology initiative framework: application to dementia." British Journal of Radiology 92, no. 1101 (September 2019): 20190365. http://dx.doi.org/10.1259/bjr.20190365.
Full textAbstract:
There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists’ experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.
31
Wu, Fang, Cong Han, Yuehong Liu, Zhiwen Liu, Xiaoxu Yang, Ye Wu, Jingwen Du, et al. "Validation of choroidal anastomosis on high-resolution magnetic resonance imaging as an imaging biomarker in hemorrhagic moyamoya disease." European Radiology 31, no. 7 (January 14, 2021): 4548–56. http://dx.doi.org/10.1007/s00330-020-07479-0.
Full text
32
Kotecha, Gopal, Hiroaki Mano, Kenji Leibnitz, Aya Nakae, Valerie Voon, Wako Yoshida, Toshio Yanagida, Mitsuo Kawato, Maria Joao Rosa, and Ben Seymour. "A NEURAL BIOMARKER FOR CHRONIC PAIN BASED ON DECODED BRAIN NETWORKS." Journal of Neurology, Neurosurgery & Psychiatry 86, no. 11 (October 14, 2015): e4.108-e4. http://dx.doi.org/10.1136/jnnp-2015-312379.20.
Full textAbstract:
The lack of a biomarker for chronic pain remains an important impediment to clinical and translational pain research. The problem stems from the multiple parallel but subtle abnormalties thought to represent the chronic pain state, yielding the emerging view of chronic pain as a ‘network disorder’. This suggests analysis approaches that aim to identify distributed patterns of data (multivariate, machine learning methods) might offer the best opportunity to discover biomarkers. Here, we performed a multi-center functional brain imaging study to record state functional brain networks resting in 41 patients with chronic back pain and 33 healthy control subjects. We calculated with functional covariance matrix from 160 regions of interest, and used Sparse Multinomial Logistic Regression to classify subjects as patient or control using a leave-one-out cross validation. Diagnostic accuracy was 91.9%, with sensitivity and specificity 90.2% and 93.9% respectively. We then used graph theoretic measures to characterise the pattern of network differences between the groups, and showed that the chronic pain state was associated with disrupted network ‘assortativity’. These data provide evidence to support an accurate functional biomarker of chronic pain, and open the door to the development of translatable biomarkers using similar methodologies in animals.
33
Nishi, Hidehisa, Naoya Oishi, Akira Ishii, Isao Ono, Takenori Ogura, Tadashi Sunohara, Hideo Chihara, et al. "Deep Learning–Derived High-Level Neuroimaging Features Predict Clinical Outcomes for Large Vessel Occlusion." Stroke 51, no. 5 (May 2020): 1484–92. http://dx.doi.org/10.1161/strokeaha.119.028101.
Full textAbstract:
Background and Purpose— For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods— This multicenter retrospective study included patients with anterior circulation large vessel occlusion treated with mechanical thrombectomy between 2013 and 2018. We designed a 2-output deep learning model based on convolutional neural networks (the convolutional neural network model). This model employed encoder-decoder architecture for the ischemic lesion segmentation, which automatically extracted high-level feature maps in its middle layers, and used its information to predict the clinical outcome. Its performance was internally validated with 5-fold cross-validation, externally validated, and the results compared with those from the standard neuroimaging biomarkers Alberta Stroke Program Early CT Score and ischemic core volume. The prediction target was a good clinical outcome, defined as a modified Rankin Scale score at 90-day follow-up of 0 to 2. Results— The derivation cohort included 250 patients, and the validation cohort included 74 patients. The convolutional neural network model showed the highest area under the receiver operating characteristic curve: 0.81±0.06 compared with 0.63±0.05 and 0.64±0.05 for the Alberta Stroke Program Early CT Score and ischemic core volume models, respectively. In the external validation, the area under the curve for the convolutional neural network model was significantly superior to those for the other 2 models. Conclusions— Compared with the standard neuroimaging biomarkers, our deep learning model derived a greater amount of prognostic information from pretreatment neuroimaging data. Although a confirmatory prospective evaluation is needed, the high-level imaging features derived by deep learning may offer an effective prognostic imaging biomarker.
34
Valable, Samuel, Aurélien Corroyer-Dulmont, Ararat Chakhoyan, Lucile Durand, Jérôme Toutain, Didier Divoux, Louisa Barré, et al. "Imaging of brain oxygenation with magnetic resonance imaging: A validation with positron emission tomography in the healthy and tumoural brain." Journal of Cerebral Blood Flow & Metabolism 37, no. 7 (October 4, 2016): 2584–97. http://dx.doi.org/10.1177/0271678x16671965.
Full textAbstract:
The partial pressure in oxygen remains challenging to map in the brain. Two main strategies exist to obtain surrogate measures of tissue oxygenation: the tissue saturation studied by magnetic resonance imaging (StO2-MRI) and the identification of hypoxia by a positron emission tomography (PET) biomarker with 3-[18F]fluoro-1-(2-nitro-1-imidazolyl)-2-propanol ([18F]-FMISO) as the leading radiopharmaceutical. Nonetheless, a formal validation of StO2-MRI against FMISO-PET has not been performed. The objective of our studies was to compare the two approaches in (a) the normal rat brain when the rats were submitted to hypoxemia; (b) animals implanted with four tumour types differentiated by their oxygenation. Rats were submitted to normoxic and hypoxemic conditions. For the brain tumour experiments, U87-MG, U251-MG, 9L and C6 glioma cells were orthotopically inoculated in rats. For both experiments, StO2-MRI and [18F]-FMISO PET were performed sequentially. Under hypoxemia conditions, StO2-MRI revealed a decrease in oxygen saturation in the brain. Nonetheless, [18F]-FMISO PET, pimonidazole immunohistochemistry and molecular biology were insensitive to hypoxia. Within the context of tumours, StO2-MRI was able to detect hypoxia in the hypoxic models, mimicking [18F]-FMISO PET with high sensitivity/specificity. Altogether, our data clearly support that, in brain pathologies, StO2-MRI could be a robust and specific imaging biomarker to assess hypoxia.
35
Beattie, Erin, Jeffery Edmiston, Patrudu Makena, Elizabeth Mason, Mike McEwan, and Krishna Prasad. "Review of recent lung biomarkers of potential harm/effect for tobacco research." F1000Research 10 (December 17, 2021): 1293. http://dx.doi.org/10.12688/f1000research.55411.1.
Full textAbstract:
Biomarkers of potential harm (BoPH) are indicators of biological perturbations which may contribute to the pathophysiology of disease. In this review, we critically assessed the published data on lung-related BoPH in human lung disease for potential use in evaluating the effects of tobacco and nicotine products. A Scopus literature search was conducted on lung disease biomarkers used in a clinical setting over the last 10 years. We identified 1171 papers which were further screened using commercial software (Sciome SWIFT-Active Screener) giving 68 publications that met our inclusion criteria (data on the association of the biomarker with cigarette smoking, the impact of smoking cessation on the biomarker, and differences between smokers and non-smokers), the majority of which investigated chronic obstructive pulmonary disease. Several physiological and biochemical measures were identified that are potentially relevant for evaluating the impact of tobacco products on lung health. Promising new candidates included blood biomarkers, such as surfactant protein D (SP-D), soluble receptor for advanced glycation end products (sRAGE), skin autofluorescence (SAF), and imaging techniques. These biomarkers may provide insights into lung disease development and progression; however, all require further research and validation to confirm their role in the context of tobacco and nicotine exposure, their time course of development and ability to measure or predict disease progression.
36
Hemstreet, G. P., G. P. Casale, D. Huang, J. Tian, M. A. Simpson, Z. Kaleem, L. M. Smith, J. E. Elkahwaji, and S. L. Johansson. "Validation of quantitative fluorescence imaging analysis (QFIA) of β-catenin in archived prostate tissues for cancer biomarker discovery." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 15575. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.15575.
Full textAbstract:
15575 Background: The objective of this study was to develop a sensitive tissue proteomic assay for quantifying proteins (e.g. β- catenin) in tissues and prostate core biopsies by QFIA. A second objective was to apply the validated methodology to quantify other biomarkers for prostate cancer risk assessment, and to study oxidative stress in BPH, prostate cancer, the cancer field, and in inflammatory prostatitis in animals and human prostatitis. Methods: Biopsies from controls and cancer patients were depariffinized, the antigen retrieved, and labeled with a robotic BioGenex Stainer with a primary mouse monoclonal antibody (1/100) to β-catenin. A secondary goat anti-mouse IgG antibody (2° Ab 1/100) coupled with Alexa Fluor 568 was used as the indicator system. The Leica Fluorescence Microscope was calibrated and standardized with fluorescence beads, LNCAP cells, and BPH tissue sections. QFIA of β-catenin was validated by RPPA methacarn fixed tissues. Adjacent 4-micron tissue sections were analyzed by QFIA for total β-catenin content by RPPA. Results: Reproducibility was 10% or less for the LNCAP cells and BPH tissue controls. Adjacent tissue sections assayed by QFIA and RPPA exhibited a strong linear correlation (r=.97) as did tissues fixed in methacarn vs.10% buffered-formalin and assayed by QFIA (r=0.84). For the core biopsy specimens the average MPI (AMPI) from 40 to 200 acini was quantified. The AMPI of cancerous acini (CA) compared to nomal acini (NA) was reduced in 37 of 42 cases p<.02. ROC plots revealed that β-catenin expression in NAA identified 42% (95%CI 25 - 52%) of cancer cases, with 88% (95% CI 80%-96%) specificity. The tissue QFIA method was used to quantify oxidative stress biomarkers (MnSOD, HNE, 8-OHdG) and other field disease biomarkers (Connexin42, and UDP-glucose dehydrogenase (UGDH) in BPH, prostate cancer, and in prostatitis. Conclusions: A tissue based QFIA method has been developed for the quantification of β-catenin in prostate core biopsy specimens, and the method has been validated by RPPA. Assay of β-catenin in prostate core biopsy specimens shows promise as a potential biomarker for a profile defining individuals at risk for prostate cancer and studying oxidative stress in relation to the pathogenesis of disease. No significant financial relationships to disclose.
37
Abignano, G., S. Aydin, C. Castillo-Gallego, D. Woods, A. Meekings, D. McGonagle, P. Emery, and F. Del Galdo. "OP0228 Optical coherence tomography validation: A new quantitative imaging biomarker for affected skin in scleroderma." Annals of the Rheumatic Diseases 71, Suppl 3 (June 2013): 133.1–133. http://dx.doi.org/10.1136/annrheumdis-2012-eular.1911.
Full text
38
Min, Kyueng-Whan, Joo-Young Bang, Kwang Pyo Kim, Wan-Seop Kim, Sang Hwa Lee, Selina Rahman Shanta, Jeong Hwa Lee, et al. "Imaging Mass Spectrometry in Papillary Thyroid Carcinoma for the Identification and Validation of Biomarker Proteins." Journal of Korean Medical Science 29, no. 7 (2014): 934. http://dx.doi.org/10.3346/jkms.2014.29.7.934.
Full text
39
Fiebach, Jochen B., Jonas D. Stief, Ramanan Ganeshan, Benjamin Hotter, Ann-Christin Ostwaldt, Christian H. Nolte, and Kersten Villringer. "Reliability of Two Diameters Method in Determining Acute Infarct Size. Validation as New Imaging Biomarker." PLOS ONE 10, no. 10 (October 8, 2015): e0140065. http://dx.doi.org/10.1371/journal.pone.0140065.
Full text
40
Choi, Seung Won, Hwan-Ho Cho, Harim Koo, Kyung Rae Cho, Karl-Heinz Nenning, Georg Langs, Julia Furtner, et al. "Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance." Cancers 12, no. 7 (June 27, 2020): 1707. http://dx.doi.org/10.3390/cancers12071707.
Full textAbstract:
We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (“heterogenous enhancing”, “rim-enhancing necrotic”, and “cystic” subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation.
41
Paiva Maia, Yara Cristina, Thaise Gonçalves Araújo, Diego Leoni Franco, Fausto Emilio Capparelli, Vanesssa Silva Ribeiro, Patrícia Tieme Fujimura, Luanda Calábria, et al. "A novel breast cancer screening platform: An epitope-based biomarker coupled to electrochemical sensor." Journal of Clinical Oncology 33, no. 28_suppl (October 1, 2015): 9. http://dx.doi.org/10.1200/jco.2015.33.28_suppl.9.
Full textAbstract:
9 Background: The subtractive proteomic selection technology called Phage Display (PD) has been extensively used by our group in the discovery of high affinity ligands to target tissues and molecules. We have selected specific ligands against IgG purified from BC tissues, which was successfully coupled to an electrochemical sensor to detect the tumor-specific immune response. Methods: After PD selections, all immunoreactive peptide ligands were further characterized by DNA sequencing, in vitro translated and submitted to bioinformatic analyses. Further validations were performed by ELISA. We then used one synthetic peptide (SF4) for the construction of an immunosensor, which was applied to patients and control samples for final validation. Electrochemical impedance spectroscopy (EIS) was performed. Results: We have selected the F4 peptide for final validation and sensor construction due to its capability of detecting IgG in the peripheral blood and the excellent ELISA ratio BC:BBD, discriminating more than 70% of BC patients. The synthetic peptide reached a good precision in BC diagnosis (68%), but surprisingly, the selected F4 clone presented the highest sensitivity and specificity, (77.8% and 85.7%, respectively), suggesting that it can be used as a diagnostic reagent for early BC screening prior to imaging and pathological analyses. The electrochemical sensor that was built with the epitope-based peptide discriminated all IgG from BC and healthy individuals. Results with the EIS sensor demonstrated that the presence of (SF4) peptide IgG generated higher resistivity (-Z ') compared to the system containing only the peptide or the control sera. This is justified by the fact that with the immobilized biological peptide layer was correctly conjugated with the IgG forming an antigen: antibody complex that led to an increased resistance to the charge transfer system, producing a decrease in electron transfer between the iron-coupled / ferricyanide redox and the electrode surface. Conclusions: An electrochemical sensor using an epitope-based biomarker was developed, which allows for the first time BC screening using a very simple platform that could be an important auxiliary tool to mammography imaging.
42
Ellingson, Benjamin M., Mark G. Malkin, Scott D. Rand, Jennifer M. Connelly, Carolyn Quinsey, Pete S. LaViolette, Devyani P. Bedekar, and Kathleen M. Schmainda. "Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity." Journal of Magnetic Resonance Imaging 31, no. 3 (March 2010): 538–48. http://dx.doi.org/10.1002/jmri.22068.
Full text
43
Spratt, Daniel Eidelberg, Yilun Sun, Douwe Van der Wal, Shih-Cheng Huang, Osama Mohamad, Andrew J. Armstrong, Jonathan David Tward, et al. "An AI-derived digital pathology-based biomarker to predict the benefit of androgen deprivation therapy in localized prostate cancer with validation in NRG/RTOG 9408." Journal of Clinical Oncology 40, no. 6_suppl (February 20, 2022): 223. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.223.
Full textAbstract:
223 Background: The current standard of care for men with intermediate- and high-risk localized prostate cancer treated with radiotherapy (RT) is the addition of androgen deprivation therapy (ADT). Presently, there are no validated predictive biomarkers to guide ADT use or duration in such men. Herein, we train and validate the first predictive biomarker for ADT use in prostate cancer using multiple phase III NRG Oncology randomized trials. Methods: Pre-treatment biopsy slides were digitized from five phase III NRG Oncology randomized trials of men receiving RT with or without ADT. The training set to develop the artificial intelligence (AI)-derived predictive biomarker included NRG/RTOG 9202, 9413, 9910, and 0126, and was trained to predict distant metastasis (DM). A multimodal deep learning architecture was developed to learn from both clinicopathologic and digital imaging histopathology data and identify differential outcomes by treatment type. After the model was locked, an independent biostatistician performed validation on NRG/RTOG 9408, a phase III randomized trial of RT +/- 4 months of ADT. The DM rates were calculated using cumulative incidence functions in biomarker positive and negative groups, and biomarker-treatment interaction was assessed using Fine-Gray regression such that death without DM was treated as a competing event. Results: Clinical and histopathological data was available for 5,654 of 7,957 eligible patients (71.1%). The training cohort included 3,935 patients and had a median follow-up of 13.6 years (IQR [10.2, 17.7]). After the AI-derived predictive ADT classifier was trained, it was validated in NRG/RTOG 9408 (n = 1719, median follow-up 17.6 years, IQR [15.0, 19.7]). In the NRG/RTOG 9408 validation cohort that had digital histopathology data, ADT significantly improved DM (HR 0.62, 95% CI [0.44, 0.87], p = 0.006), consistent with the published trial results. The biomarker-treatment interaction was significant (p-value = 0.0021). In patients with AI-biomarker positive disease (n = 673, 39%), ADT had a greater benefit compared to RT alone (HR 0.33, 95% CI [0.19, 0.57], p < 0.001). In the biomarker negative subgroup (n = 1046, 61%), the addition of ADT did not improve outcomes over RT alone (HR 1.00, 95% CI [0.64, 1.57], p = 0.99). The 15-year DM rate difference between RT versus RT+ADT in the biomarker negative group was 0.3%, vs biomarker positive group 9.4%. Conclusions: We have successfully validated in a phase III randomized trial the first predictive biomarker of ADT benefit with RT in localized intermediate risk prostate cancer using a novel AI-derived digital pathology-based platform. This AI-derived predictive biomarker demonstrates that a majority of patients treated with RT on NRG/RTOG 9408 did not require ADT and could have avoided the associated costs and side effects of this treatment.
44
Won Choi, Seung, Hwan-ho Cho, Harim Koo, Kyung rae Cho, Karl-Heinz Nenning, Georg Langs, Julia Furtner, et al. "NIMG-20. MULTI-HABITAT RADIOMICS UNRAVELS DISTINCT PHENOTYPIC SUBTYPES OF GLIOBLASTOMA WITH CLINICAL AND GENOMIC SIGNIFICANCE." Neuro-Oncology 22, Supplement_2 (November 2020): ii151. http://dx.doi.org/10.1093/neuonc/noaa215.633.
Full textAbstract:
Abstract BACKGROUNDS We aimed to evaluate the potential of radiomics as an imaging biomarker for GBM patients and explore the molecular rationale behind radiomics by radio-genomics approach. METHODS A total of 144 primary GBM patients were included in this study as a training cohort. Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model and validated this model using an independent validation cohort (56 patients from Vienna). With the selected radiomics features, GBM patients were consensus clustered to reveal inherent phenotypic subtypes. The subtypes were further explored in terms of genomic signatures. RESULTS GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (‘heterogenous enhancing’, ‘rim-enhancing necrotic’, and ‘cystic’ subtype). Multi-variate cox regression analysis confirmed that radiomics subtype as an independent prognostic factor. Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation & flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). CONCLUSIONS The present study confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation. Imaging subtypes derived from radiomics successfully recapitulate the genomic underpinnings of GBM tumors and in turn reinforce their potential as a prognostic biomarker.
45
Lassau, Nathalie, Serge Koscielny, Sophie Taieb, Joelle Lacroix, Richard Aziza, Florence Joly, Christine Chevreau, Sylvie Negrier, Gwenaelle Gravis, and Bernard J. Escudier. "Validation of imaging biomarker in a multicentric study to predict PFS in mRCC treated with TKI." Journal of Clinical Oncology 32, no. 4_suppl (February 1, 2014): 526. http://dx.doi.org/10.1200/jco.2014.32.4_suppl.526.
Full textAbstract:
526 Background: Tyrosine kinase inhibitors including sunitinib are the most effective treatments of metastatic renal cell carcinoma (mRCC). A multi-centric study of 539 patients (different tumors treated anti-angiogenic treatments) evaluating dynamic contrast-enhanced ultrasound (DCE-US), showed that a decrease of AUC (Area under the curve) correlated to the blood volume at one month is predictive of response. Our first objective was to validate the correlation between this parameter and the PFS in a sub-group of mRCC treated with Sunitinib The second objective was to study the variability of AUC. Methods: Each Patient had CT-scan every 2 months in order to evaluate the Response assessment using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1).DCE-US were performed at baseline and at D30. At each examination, we quantified 7 DCE-US parameters after bolus injection of contrast agent and mathematical modelization of raw linear data recorded during 3 minutes. We also estimated the variation between baseline and D30. The main endpoint was progression free survival assessed according to RECIST. We first selected the best parameters. We studied the trend between the parameter value and freedom from progression. After, the best cut-points were searched through a grid search. The best single cut-point was that with the lowest P-value for progression free survival. We performed this analysis in the sub-group of patients with mRCC treated with sunitinib. Morever, we studied the variability of AUC in 30 other patients treated with TKI . We performed in this group 2 DCE-US the same day before and after lunch. Results: A total of 81 mRCC patients treated with sunitinib were selected. All had DCE-US at baseline and one month. The median of follow-up was 18 months. For DCE-US, the decrease of 90 % of AUC at D 30 was correlated to the PFS (p =0.03). The difference of PFS between the groups defined by this cut-point was 4 months (bad responders) and 14 months (good responders). The results of variability are on-going. Conclusions: The decrease of more than 90% of AUC with DCE-US at one month is a potential predictive biomarker of response in mRCC patients treated with sunitinib. The results of variability of this parameter will be also presented. Clinical trial information: no. 912346.
46
Mureb, Monica, Rajan Jain, Laila Poisson, Ingrid Aguiar Littig, Lucidio Nunes Neto, Chih-Chin Wu, Victor Ng, et al. "NIMG-09. NONINVASIVE PERFUSION IMAGING BIOMARKER OF MALIGNANT GENOTYPE IN ISOCITRATE DEHYDROGENASE MUTANT GLIOMAS." Neuro-Oncology 21, Supplement_6 (November 2019): vi163. http://dx.doi.org/10.1093/neuonc/noz175.681.
Full textAbstract:
Abstract OBJECTIVE A small subset of IDHmut astrocytomas behave aggressively, similarly to IDHwt glioblastoma. Genomic correlates of poor prognosis IDHmut astrocytomas include global relative DNA hypomethylation, MYCN amplification copy number variation (CNV) abundance, and CDKN2A/B homozygous deletion. We sought to identify a non-invasive imaging correlate of poor prognosis IDHmut astrocytomas. METHODS 30 IDHmut astrocytomas (NYU=18, TCGA=12) were included. Relative cerebral blood volume was obtained from 4 regions of interest within the highest perfusion areas. Genomic CNV analysis and CDKN2A/B copy number status was obtained using Illumina Infinium 450k or Illumina EPIC methylation array data. Associations between rCBV, survival, and genomic alterations were evaluated. An additional 22 IDHmut astrocytomas were obtained from another institution for validation. RESULTS CNV stable (CNV-S, n=14) astrocytomas demonstrated significantly lower mean rCBV than and CNV unstable (CNV-U, n=16) (P=0.0.0155) ones. IDHmut astrocytomas with CDKN2A/B homozygous deletion (n=7) had higher rCBV (3.9 ± 2.3, mean ± SD) compared to those without this alteration (n=22; 1.8 ± 1.9, mean ± SD; P=0.0489). In the validation set, there was no significant evidence in rCBV between CNV-S and CNV-U tumors (P=0.3976). These results were sensitive to a single outlier in the CNV-S group. Excluding this case with rCBV=7.15, the CNV-S tumors demonstrated significantly lower mean rCBV (P=0.0279). IDHmut astrocytomas with CDKN2A/B homozygous deletion (n=9) tended to have higher rCBV (4.2 ± 1.8, mean ± SD) compared to those without this alteration (n=16; 2.5 ± 1.5, mean ± SD; P=0.0757, t-test). This led to significant discrimination (P=0.0328, Wilcoxon). CONCLUSION Our study is the first to identify a non-invasive imaging biomarker for prognostic genomic alterations in IDHmut astrocytomas. Greater CNV abundance and/or CDKN2A/B homozygous deletion have higher rCBV and worse prognosis. Our methods can be applied to standard clinical practice and may enhance informed treatment decisions in the preoperative and immediate postoperative settings.
47
Rodrigues, Pedro M., Arndt Vogel, Marco Arrese, Domingo C. Balderramo, Juan W. Valle, and Jesus M. Banales. "Next-Generation Biomarkers for Cholangiocarcinoma." Cancers 13, no. 13 (June 28, 2021): 3222. http://dx.doi.org/10.3390/cancers13133222.
Full textAbstract:
The increasing mortality rates of cholangiocarcinoma (CCA) registered during the last decades are, at least in part, a result of the lack of accurate non-invasive biomarkers for early disease diagnosis, making the identification of patients who might benefit from potentially curative approaches (i.e., surgery) extremely challenging. The obscure CCA pathogenesis and associated etiological factors, as well as the lack of symptoms in patients with early tumor stages, highly compromises CCA identification and to predict tumor development in at-risk populations. Currently, CCA diagnosis is accomplished by the combination of clinical/biochemical features, radiological imaging and non-specific serum tumor biomarkers, although a tumor biopsy is still needed to confirm disease diagnosis. Furthermore, prognostic and predictive biomarkers are still lacking and urgently needed. During the recent years, high-throughput omics-based approaches have identified novel circulating biomarkers (diagnostic and prognostic) that might be included in large, international validation studies in the near future. In this review, we summarize and discuss the most recent advances in the field of biomarker discovery in CCA, providing new insights and future research directions.
48
Schell, Marianne, Irada Pflüger, Gianluca Brugnara, Fabian Isensee, Ulf Neuberger, Martha Foltyn, Tobias Kessler, et al. "Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial." Neuro-Oncology 22, no. 11 (May 12, 2020): 1667–76. http://dx.doi.org/10.1093/neuonc/noaa120.
Full textAbstract:
Abstract Background This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). Methods A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). Results ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10–6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). Conclusions ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
49
Xiao, Yudong, Tao Wang, Wei Deng, Le Yang, Bin Zeng, Xiaomei Lao, Sien Zhang, et al. "Data mining of an acoustic biomarker in tongue cancers and its clinical validation." Cancer Medicine 10, no. 11 (May 2, 2021): 3822–35. http://dx.doi.org/10.1002/cam4.3872.
Full text
50
Tan, Hongna, Yaping Wu, Fengchang Bao, Jing Zhou, Jianzhong Wan, Jie Tian, Yusong Lin, and Meiyun Wang. "Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer." British Journal of Radiology 93, no. 1111 (July 2020): 20191019. http://dx.doi.org/10.1259/bjr.20191019.
Full textAbstract:
Objective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. Results: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591–0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). Conclusion: The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. Advances in knowledge: ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.