Deep-Radiomic Fusion for Early Detection of Pancreatic Ductal Adenocarcinoma

Leveraging the complementary strengths of handcrafted radiomics and data-driven deep learning, this work develops and rigorously benchmarks three modeling streams (Models A, B and C) for pancreatic ductal adenocarcinoma (PDAC) detection on multiphase abdominal Computed Tomography (CT) scans. Model A distills hundreds of PyRadiomics descriptors to sixteen interpretable features that feed a gradient-boosted machine learning model, achieving discrimination (external AUC ≈ 0.99) with excellent calibration. Model B adopts a 3-D CBAM-ResNet-18 trained under weighted cross-entropy and mixed precision; although less accurate in isolation, it yields volumetric Grad-CAM maps that localize the tumor and provide explainability. Model C explores two fusion strategies that merge radiomics and deep embeddings: (i) a two-stage “frozen-stream” variant that locks both feature extractors and learns only a lightweight gating block plus classifier, and (ii) a full end-to-end version that allows the CNN’s adaptor layer to co-train with the fusion head. The frozen approach surpasses the single stream, whereas the end-to-end model reports external AUC of 0.987, balanced sensitivity/specificity above 0.93, and a Brier score below 0.05, while preserving clear Grad-CAM alignment with radiologist-drawn masks. Results demonstrate that a carefully engineered deep-radiomic fusion pipeline can deliver accurate, well-calibrated and interpretable PDAC triage directly from routine CT. Our contributions include a stability-verified 16-feature radiomic signature, a novel deep-radiomic fusion design that improves robustness and interpretability across vendors and a fully guideline-aligned, openly released pipeline for reproducible PDAC detection on routine CT.