End-to-end modeling for predicting and estimating radiomics: application to gray level co-occurrence matrices in CT

Grace J. Gang; Radhika Deshpande; J. Webster Stayman

doi:10.1117/12.2582150

15 February 2021 End-to-end modeling for predicting and estimating radiomics: application to gray level co-occurrence matrices in CT

Grace J. Gang, Radhika Deshpande, J. Webster Stayman

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 1159509 (2021) https://doi.org/10.1117/12.2582150
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

While radiomics models are finding increased use in computer-aided diagnostics and as imaging biomarkers for inference and discovery, their utility in computed tomography (CT) is limited by variability of the image properties produced by different CT scanners, imaging protocols, patient anatomy, and an increasingly diverse range of reconstruction and post-processing software. While these effects can be mitigated with careful data curation and standardization of protocols, this is impractical for diverse sources of image data. In this work, we propose to generalize traditional end-to-end imaging system models to include radiomics calculation as an explicit stage. Such a model permits both prediction of the undesirable variability of radiomics, but also forms a basis for inverting the process to estimate the true underlying radiomics. This framework has the potential to provide for standardization of radiomics across imaging conditions permitting more widespread application of radiomics models; larger, more diverse image databases; and improved diagnoses and inferences based on those standardized metrics. We apply this framework to a large class of popular radiomics based on the Gray Level Co-occurrence matrix under conditions of imaging system that are well describe by traditional linear systems approaches as well as nonlinear systems for which traditional analytic models do not apply.

Conference Presentation

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Grace J. Gang, Radhika Deshpande, and J. Webster Stayman "End-to-end modeling for predicting and estimating radiomics: application to gray level co-occurrence matrices in CT", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 1159509 (15 February 2021); https://doi.org/10.1117/12.2582150

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