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

ABSTRACT:

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 liv

SUBMITTER: Avram MF 

PROVIDER: S-EPMC12753320 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature