Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons
Document Type
Article
Publication Date
2014
Keywords
quantitative imaging, imaging biomarkers, image metrics, bias, precision, repeatability, reproducibility, agreement
Digital Object Identifier (DOI)
https://doi.org/10.1177/0962280214537390
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Statistical Methods in Medical Research, v. 24, issue 1, p. 68-106
Scholar Commons Citation
Obuchowski, Nancy A.; Reeves, Anthony P.; Huang, Erich P.; Wang, Xiao-Feng; Buckler, Andrew J.; Kim, Hyun J.; Barnhart, Huiman X.; Jackson, Edward F.; Giger, Maryellen L.; Pennello, Gene; Toledano, Alicia Y.; Kalpathy-Cramer, Jayashree; Apanasovich, Tatiyana V.; Kinahan, Paul E.; Myers, Kyle J.; Goldgof, Dmitry B.; Barboriak, Daniel P.; Gillies, Robert J.; Schwartz, Lawrence H.; and Sullivan, Daniel C., "Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons" (2014). Computer Science and Engineering Faculty Publications. 138.
https://digitalcommons.usf.edu/esb_facpub/138
