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. 2023 Sep 4;13:1218128. doi: 10.3389/fonc.2023.1218128

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Copyright © 2023 Cen, Wang, Wang, Wen, Liu, Li, Wu, Huang, Ma, Liu, Wu and Han

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Schema shows radiomics workflow. Patients data were collected. Region of interests (ROIs) were manually delineated along the entire tumor outline on all contiguous slices, and features were extracted from three-dimensional ROIs. The least absolute shrinkage and selection operator were applied to select features. The models were constructed to discriminate high-grade and low-grade PDAC and predict overall survival. The performance of the models was evaluated. PDAC, pancreatic ductal adenocarcinoma; CECT, contrast-enhanced CT; HGrad, histological grading radiomics score; HGcli, histological grading clinical model; HGnom, histological grading nomogram; Pcli, prognostic clinical model; Pnom, prognostic nomogram; CA12-5, carbohydrate antigen 12-5.