Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.


Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Obuchowski, NA; Reeves, AP; Huang, EP; Wang, X-F; Buckler, AJ; Kim, HJG; Barnhart, HX; Jackson, EF; Giger, ML; Pennello, G; Toledano, AY; Kalpathy-Cramer, J; Apanasovich, TV; Kinahan, PE; Myers, KJ; Goldgof, DB; Barboriak, DP; Gillies, RJ; Schwartz, LH; Sullivan, DC; Algorithm Comparison Working Group,

Published Date

  • February 2015

Published In

Volume / Issue

  • 24 / 1

Start / End Page

  • 68 - 106

PubMed ID

  • 24919829

Pubmed Central ID

  • 24919829

Electronic International Standard Serial Number (EISSN)

  • 1477-0334

Digital Object Identifier (DOI)

  • 10.1177/0962280214537390


  • eng

Conference Location

  • England