Peer-Reviewed Publication
Acad Radiol2023;30(2):196-214.February 1, 2023Journal Article
Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.
Erich P Huang1, Gene Pennello2, Nandita M deSouza3, Xiaofeng Wang4, Andrew J Buckler5, Paul E Kinahan6, Huiman X Barnhart7, Jana G Delfino2, Timothy J Hall8, David L Raunig9, Alexander R Guimaraes10, Nancy A Obuchowski4
1Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, MSC 9735, Bethesda, MD 20892-9735. Electronic address: erich.huang@nih.gov.
2Center for Devices and Radiological Health, US Food and Drug Administration.
3Division of Radiotherapy and Imaging, The Institute of Cancer Research (London, UK), European Imaging Biomarkers Alliance.
4Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation.
5Elucid Bioimaging, Inc.
6Department of Radiology, University of Washington.
7Department of Biostatistics and Bioinformatics, Duke University.
8Department of Medical Physics, University of Wisconsin, Madison.
9Data Science Institute, Statistical and Quantitative Sciences, Takeda.
10Department of Diagnostic Radiology, Oregon Health and Sciences University.
Abstract
Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspe…
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