MINI REVIEW article
Front. Oncol.
Sec. Cancer Epidemiology and Prevention
This article is part of the Research TopicAdvanced Machine Learning Techniques in Cancer Prognosis and ScreeningView all 14 articles
The Place of Advanced Machine Learning Techniques in Building Pancreatic Adenocarcinoma Survival and Recurrence Prognosis Models
Provisionally accepted
Mihaela-Flavia Avram*
Daniela-Cornelia Lazăr
MIHAELA IOANA MARIȘ
Alexandru Cucui-Cozma
Marius-Sorin Murariu- Victor Babes University of Medicine and Pharmacy, Timisoara, Romania
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Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy where traditional prognostic methods, such as TNM staging, often fail to accurately predict outcomes. This review evaluates the use of machine learning (ML) in improving 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 PDAC .Results: Various algorithms, including Random Forests, XGBoost, and Deep Learning, demonstrated superior predictive performance compared to 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 small datasets and retrospective designs. Future research requires prospective validation to translate these ML tools into clinical practice.
Keywords: artificial intelligence, Pancreatic adenocarcinoma, Advanced Machine Learning, Cancer survival prediction, cancer recurrence prediction, machine learning cancer prediction
Received: 18 Oct 2025; Accepted: 01 Dec 2025.
Copyright: © 2025 Avram, Lazăr, MARIȘ, Cucui-Cozma and Murariu. 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: Mihaela-Flavia Avram
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.