Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Journal Article (Journal Article;Review)

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 aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Full Text

Duke Authors

Cited Authors

  • Huang, EP; Pennello, G; deSouza, NM; Wang, X; Buckler, AJ; Kinahan, PE; Barnhart, HX; Delfino, JG; Hall, TJ; Raunig, DL; Guimaraes, AR; Obuchowski, NA

Published Date

  • February 2023

Published In

Volume / Issue

  • 30 / 2

Start / End Page

  • 196 - 214

PubMed ID

  • 36273996

Pubmed Central ID

  • PMC9825642

Electronic International Standard Serial Number (EISSN)

  • 1878-4046

Digital Object Identifier (DOI)

  • 10.1016/j.acra.2022.09.018


  • eng

Conference Location

  • United States