@article{LI2025, 
author = {Ke LI and Jiafei CHEN and Jing YANG and Wei CHEN},
title = {Clinical application of combined CT radiomics and clinical features in survival prediction for pancreatic ductal adenocarcinoma patients},
year = {2025},
journal = {Journal of Army Medical University},
volume = {47},
number = {14},
pages = {1587-1594},
keywords = {pancreatic ductal adenocarcinoma, radiomics, prognosis prediction},
url = {https://www.sciopen.com/article/10.16016/j.2097-0927.202503004},
doi = {10.16016/j.2097-0927.202503004},
abstract = {ObjectiveTo develop a CT radiomics-based prediction model for prognosis of pancreatic ductal adenocarcinoma (PDAC) in order to provide evidence for individualized treatment decisions.MethodsA retrospective study was carried on 118 PDAC patients admitted in the First Affiliated Hospital of Army Medical University between January 2020 and December 2023. They were assigned into a training group (n=83) and a validation group (n=35) at a 7∶3 ratio. ITK-SNAP software was used to perform 3-D segmentation on the preoperatively enhanced arterial phase CT images, and radiomic features were extracted using pyradiomics. High-reproducibility features were selected through ICC analysis (>0.85), and core features were determined using LASSO regression to construct the Rad-score. Cox regression analysis was employed to develop both a radiomics model and a model integrating radiomic and clinical features for predicting overall survival in PDAC patients. Receiver operating characteristic (ROC) curves and calibration curves were plotted to evaluate the prognostic models for survival prediction.ResultsFrom 1453 extracted radiomic features, 7 core features were finally selected to construct the Rad-score. The radiomics prediction model based on the Rad-score achieved an AUC value of 0.796 (95%CI: 0.702~0.890) and 0.744 (95%CI: 0.589~0.899) for 1-year survival prediction in the training and validation groups, respectively. The integrated model combining 2 types of features together demonstrated improved performance with an AUC value of 0.906 (95%CI: 0.842~0.970) and 0.872 (95%CI: 0.753~0.992) in the 2 groups. Calibration curve analysis indicated good prediction accuracy for both models.ConclusionBoth the CT radiomics-based model and the integrated model incorporating clinical features demonstrate good predictive performance for survival outcomes.}
}