Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons
Year: 2014
Type: article
Abstract: Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research.
Authors: Nancy A Obuchowski, Anthony P Reeves, Erich P Huang, Xiao-Feng Wang, Andrew J Buckler, Hyun J (Grace) Kim, Huiman X Barnhart, Edward F Jackson, Maryellen L Giger, Gene Pennello, Alicia Y Toledano, Jayashree Kalpathy-Cramer, Tatiyana V Apanasovich, Paul E Kinahan, Kyle J Myers, Dmitry B Goldgof, Daniel P Barboriak, Robert J Gillies, Lawrence H Schwartz, Daniel C Sullivan, (for the Algorithm Comparison Working Group)
Institutions: Cleveland Clinic, Cornell University, National Institutes of Health, Elucid Bioimaging, University of California, Los Angeles, Duke University, University of Wisconsin–Madison, University of Chicago, United States Food and Drug Administration, Harvard University, Massachusetts General Hospital, George Washington University, University of Washington, University of South Florida, Moffitt Cancer Center, Columbia University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2072484199