Clinical application of combined CT radiomics and clinical features in survival prediction for pancreatic ductal adenocarcinoma patients
10.16016/j.2097-0927.202503004
- VernacularTitle:CT影像组学联合临床特征对胰腺导管腺癌患者生存预测的应用研究
- Author:
Ke LI
1
;
Jiafei CHEN
;
Jing YANG
;
Wei CHEN
Author Information
1. 陆军军医大学(第三军医大学)第一附属医院放射科
- Keywords:
pancreatic ductal adenocarcinoma;
radiomics;
prognosis prediction
- From:
Journal of Army Medical University
2025;47(14):1587-1594
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a CT radiomics-based prediction model for prognosis of pancreatic ductal adenocarcinoma(PDAC)in order to provide evidence for individualized treatment decisions.Methods A 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.Results From 1 453 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.Conclusion Both the CT radiomics-based model and the integrated model incorporating clinical features demonstrate good predictive performance for survival outcomes.
