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

Huang E.P.; Pennello G.; deSouza N.M.Wang X.Buckler A.J.Kinahan P.E.Barnhart H.X.Delfino J.G.Hall T.J.Raunig D.L.Guimaraes A.R.Obuchowski N.A.

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

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

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? 2022Combinations 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.

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《Academic radiology》 |2023年第2期|196-214|共19页
作者
Huang E.P.; Pennello G.; deSouza N.M.Wang X.Buckler A.J.Kinahan P.E.Barnhart H.X.Delfino J.G.Hall T.J.Raunig D.L.Guimaraes A.R.Obuchowski N.A.;
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Department of Biostatistics and Bioinformatics Duke University;

Center for Devices and Radiological Health US Food and Drug Administration;

Department of Medical Physics University of WisconsinData Science Institute Statistical and Quantitative SciencesDepartment of Diagnostic Radiology Oregon Health and Sciences UniversityDepartment of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic FoundationDivision of Cancer Treatment and Diagnosis National Cancer Institute National Institutes of HealthDivision of Radiotherapy and Imaging The Institute of Cancer Research (London UK) European ImagingElucid Bioimaging Inc.Department of Radiology University of Washington;

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正文语种英语
中图分类放射医学;
关键词
model development; model validation; quantitative imaging; technical performance;

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

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