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

Erich P. Huang, Gene Pennello, Nandita M. deSouza, Xiaofeng Wang, Andrew J. Buckler, Paul E. Kinahan, Huiman X. Barnhart, Jana G. Delfino, Timothy J. Hall, David L. Raunig, Alexander R. Guimaraes, Nancy A. Obuchowski

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

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 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.

Original languageEnglish (US)
Pages (from-to)196-214
Number of pages19
JournalAcademic radiology
Volume30
Issue number2
DOIs
StatePublished - Feb 2023

Keywords

  • model development
  • model validation
  • quantitative imaging
  • technical performance

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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