From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction

Date

2025-07-01

Author

Farinella, Riccardo
Felici, Alessio
Peduzzi, Giulia
Testoni, Sabrina Gloria Giulia
Costello, Eithne
Aretini, Paolo
Blazquez-Encinas, Ricardo
ÖZ, ELİF
Pastore, Aldo
Tacelli, Matteo
Otlu Sarıtaş, Burçak
Campa, Daniele
Gentiluomo, Manuel

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Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets—spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

Subject Keywords

Artificial intelligence, Early detection, Genetic susceptibility, Pancreatic cancer, Risk stratification

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105001255686&origin=inward
https://hdl.handle.net/11511/114304

Collections

Graduate School of Informatics, Article