AUTHOR=Fan Lingling , Kong Shujing , Deng Yizhou , Wang Yunfan , Yan Xiancheng , Jiang Chao , Tao Li , Wang Weimin 

TITLE=Compare the prognosis of pancreatic cancer patients with different treatment modalities and use machine learning methods to build predictive models

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1629324

DOI=10.3389/fmed.2025.1629324

ISSN=2296-858X

ABSTRACT=BackgroundPancreatic cancer (PC) is highly refractory to most treatments. Multimodal treatment, combining several types of therapies, is likely to benefit PC patients. However, it remains unclear which multimodal treatment is most effective and how to predict outcomes from different combinations. This study compared overall survival among PC patients receiving chemotherapy alone (C), immunotherapy combined with chemotherapy (CI), radiotherapy combined with chemotherapy (CR), and triple-combination therapy (CRI). A machine learning-based predictive model between monomodal and multimodal therapy was established using 3 years of clinical follow-up data.MethodsWe retrospectively analyzed 125 cases of PC patients treated at Yixing People’s Hospital from January 2014 to June 2024 (C, n = 50; CI, n = 38; CR, n = 18; CRI, n = 19). The group CI, CR and CRI were merged and defined as multiple modalities (MM) group (n = 75), while the group C was defined as single modality (SM) treatment group (n = 50). Kaplan-Meier plots estimated the overall survival rate of each group and the survival rate of the SM group and the MM group. Cox proportional hazard models identified key prognostic factors, including cytokines and inflammation mediators. Four machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost) were used to build predictive models. SHapley Additive exPlanations (SHAP) identified significant contributors to treatment outcomes.ResultsMultimodal treatments significantly improved PC prognosis (P = 0.0025). Univariate and multivariate Cox regression analysis showed that interleukin-2 (IL-2) was a protective factor, while neutrophil-to-lymphocyte ratio (NLR) was a risk factor. This study evaluated and compared the predictive performance of four machine learning models using the classifiers such as area under curve (AUC), accuracy and F1 score, etc. In the binary classification task, RF and XGBoost models both achieved good performance compared with the other two machine learning methods. In addition, SHAP analysis also proved that IL-6 contributed the most to the machine learning models.ConclusionPC patients may benefit from more intensive multimodal therapies, which provides novel insights into predicting PC survival prognosis and highlights the potential of machine learning in biomarker identification and disease prognosis.