A Comprehensive Review of Artificial Intelligence Methods for Tumor Detection in Pancreatic Cancer Diagnosis

Abstract

Pancreatic cancer, particularly Pancreatic Ductal Adenocarcinoma (PDAC), is one of the most aggressive and lethal malignancies, with a high mortality rate mainly due to delayed diagnosis and the difficulty of detecting small tumors in early stages using conventional imaging techniques. Early detection plays a crucial role in improving patient survival; however, traditional diagnostic approaches such as computed tomography (CT), endoscopic ultrasound (EUS), and positron emission tomography–computed tomography (PET/CT) often face limitations in identifying tumors smaller than 2 cm. Recent advancements in Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL), have significantly improved medical image analysis by enabling automated feature extraction and accurate tumor detection from large scale clinical datasets. Deep learning models, particularly convolutional neural networks, have demonstrated promising performance in detecting pancreatic tumors, assisting radiologists in clinical decision making, and reducing diagnostic errors. This review provides a comprehensive overview of recent AI based approaches for tumor detection in pancreatic cancer diagnosis, highlighting advancements in imaging analysis, biomarker identification, and predictive modeling. The study also discusses neural network architectures, diagnostic accuracy reported in recent studies, and the clinical potential of AI driven systems for early detection and improved patient outcomes.