Synergistic Deep Learning Approaches for Pancreatic Cancer Diagnosis: Comparative Analysis of CNN Architectures and Multimodal Data Fusion

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest cancers due to late detection and limited therapeutic options. Recent deep learning advances, spanning CNNs, radiomics-DL hybrids, and early CNN-Transformer variants, show promise across CT/MRI/EUS/PET, yet clinical translation is hindered by single-center cohorts, scarce external validation, limited explainability, and weak integration of multiomics data. This paper offers a focused critical review and a feasibility-oriented methodological blueprint rather than a deployed system. Our contributions are threefold: (i) a structured synthesis of the PDAC literature by modality, task, and model family, highlighting recurring failure modes; (ii) an explainable multimodal framework that unifies imaging and clinical/radiomics (± omics) signals; and (iii) a concrete evaluation protocol specifying held-out external testing, task-appropriate metrics (AUROC with 95% CIs, sensitivity at 90% specificity, AUPRC; Dice/HD95; C-index/AUC <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t)$</tex>), calibration and decision-curve analysis, plus statistical comparisons. We also detail deployment constraints, including compute/VRAM reporting, inference-time targets, and lightweight distilled/quantized variants for low-resource sites, together with model and data cards and Grad-CAM exemplars to support clinical interpretation. The blueprint is intended to guide rigorous, reproducible PDAC studies and credible prospective validations.