The place of advanced machine learning techniques in building pancreatic adenocarcinoma survival and recurrence prognosis models.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, and traditional prognostic methods, such as TNM staging, often fail to accurately predict outcomes. This review evaluates the use of machine learning (ML) to improve PDAC prognosis. Methods: A systematic literature search of PubMed and Google Scholar was conducted, identifying 12 studies that applied ML algorithms to predict survival, recurrence, and metastasis in patients with PDAC. Results: Various algorithms, including Random Forests, XGBoost, and Deep Learning, demonstrated superior predictive performance compared to the TNM staging. Models using multimodal data-combining clinical, radiomic, and genomic features-yielded the highest accuracy for predicting overall survival and early liver metastasis. Conclusion: ML offers a significant advantage in analyzing complex medical data to refine risk stratification and support personalized PDAC treatment. However, current models are limited by their small datasets and retrospective designs. Future research requires prospective validation to translate these ML tools into clinical practice.