ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1644141
This article is part of the Research TopicAI-Based Prognosis Prediction and Dose Optimization Strategy in Radiotherapy for Malignant TumorsView all 12 articles
Application of Machine Learning for Prognostic Modeling in Unresectable Pancreatic Cancer Treated with Chemoradiotherapy
Provisionally accepted
- 1Henan Provincial People's Hospital, Zhengzhou, China
- 2Huazhong University of Science and Technology, Wuhan, China
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Patients with unresectable pancreatic cancer have poor outcomes despite chemoradiotherapy (CRT) . Traditional prognostic tools lack accuracy in predicting survival. This study aimed to develop an artificial intelligence (AI)-based model to improve survival prediction.We retrospectively included 214 patients treated with CRT between 2018 and 2024. Five models-Cox, LASSO, RSF, SVM, and XGBoost-were trained to predict overall survival.Model performance was evaluated using the C-index, time-dependent ROC, calibration, and decision curve analysis. SHAP was used to interpret feature importance.The median overall survival (mOS) for the entire cohort was 18.4 months (95% CI, 16.3-28.1). XGBoost showed the best performance (C-index = 0.949). It also achieved higher area under the receiver operating characteristic curves at 6 and 12 months (0.751 and 0.732) compared to other models. Calibration and clinical benefit were superior. SHAP analysis identified CA199, tumor size, platelet count, and age as the most important predictors. The model stratified patients into risk groups with significant survival differences (p < 0.001).The XGBoost-based model accurately predicted survival in unresectable pancreatic cancer patients receiving CRT. It may serve as a useful tool for personalized risk assessment and treatment planning.
Keywords: artificial intelligence, Survival, Pancreatic Cancer, Chemoradiotherapy, Model
Received: 10 Jun 2025; Accepted: 11 Aug 2025.
Copyright: © 2025 Wei, Yang and Ke. 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) or licensor 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.
* Correspondence: Shanbao Ke, Henan Provincial People's Hospital, Zhengzhou, China
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