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Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

Kiaran P. McGee, Ken‐Pin Hwang, Daniel C. Sullivan, John Kurhanewicz, Yanle Hu, Jihong Wang, Wen Li, Josef P. Debbins, E.S. Paulson, Jeffrey R. Olsen, Chia‐Ho Hua, Lizette Warner, J. Daniel, Eduardo G. Moros, Neelam Tyagi, Caroline Chung

2021Medical Physics29 citationsDOIOpen Access PDF

Abstract

PURPOSE: A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Topics & Concepts

Context (archaeology)BiomarkerMedicineRadiation therapyMagnetic resonance imagingMedical physicsRadiation oncologyPersonalized medicineOncologyBioinformaticsRadiologyBiologyBiochemistryPaleontologyAdvanced MRI Techniques and ApplicationsMedical Imaging Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging
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