From Radiomics to Transformers in Pancreatic Cancer Detection and Prognosis

Maram Almufareh^1*Samabia Tehsin²Mamoona Humayun^3*Sumaira Kausar²Asad Farooq²Haya Aldossary⁴Abeer Aljohani⁵

• ¹Jouf University Computer and Information Sciences, Sakaka, Saudi Arabia
• ²Bahria University, Islamabad, Pakistan
• ³University of Roehampton London, Roehampton, United Kingdom
• ⁴Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
• ⁵Taibah University, Medina, Saudi Arabia

ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, primarily due to late diagnosis and poor therapeutic response. Advances in artificial intelligence (AI), particularly in medical imaging and multi-modal data integration, have created new opportunities for improving early detection and personalized prognostication. This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The protocol was prospectively registered with the Open Science Framework, covering studies published between 2015 and 2025. Distinct from prior surveys that focus narrowly on specific algorithms or data types, this work introduces a generational taxonomy of AI approaches—ranging from classical radiomics-based machine learning to deep learning and contemporary transformer-based models—and maps their application to core clinical tasks such as detection, segmentation, classification, and outcome prediction. A key contribution is the integration of diverse datasets across imaging, pathology, and molecular sources; we further assess trends in availability, usage, and sample scale. We critically evaluate limitations in generalizability, external validation, model calibration, and translational readiness, and outline recommendations for multi-centre validation, standardized reporting, domain adaptation, and clinician-centred interpretability.