SCOPE: AI-Assisted Early Detection of Potentially Curable Pancreatic Neoplasms on CT from Local and Global Information

Purpose To develop SCOPE (Small-lesion COntextual Pancreatic Evaluator), a deep learning model designed to improve CT detection of small pancreatic lesions—pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (PanNETs), and cystic lesions—by integrating voxel-level features with global context.

Materials and Methods This retrospective study used three independent datasets. A development cohort of 4,065 contrast-enhanced CT scans was used to train a deep neural network that performs pancreas, ductal, and lesion segmentation with an integrated classification head. A metamodel combined segmentation-derived and global contextual signals for case-level prediction. Performance was assessed on (1) an internal holdout test set (n = 605), (2) an external multi-institutional PDAC dataset from the PANORAMA challenge (n = 2,238), and (3) an expert-curated small-lesion reader study (n = 200). Areas under the receiver operating characteristic curve (AUCs) were compared using DeLong test; sensitivities and specificities using McNemar’s test.

Results On the internal test set, SCOPE improved lesion-versus-normal AUC compared with the best segmentation baseline (0.974 [95% CI: 0.964, 0.984] vs 0.956; P = .006) and increased small-lesion sensitivity at 95% specificity (0.727 [95% CI: 0.653, 0.801] vs 0.600; P = .012). Performance gains were observed across lesion classes, with significant improvements for PDAC and PanNET detection. On the external dataset, SCOPE improved PDAC-versus-non-PDAC AUC (0.978 vs 0.861, P < .001) and achieved higher sensitivity at 90% and 95% specificity without retraining. For the small-lesion reader study, SCOPE achieved lesion-versus-normal AUC of 0.922 and performed within the range of subspecialty abdominal radiologists; SCOPE provided the correct diagnosis in 14.5% (29/200) of cases in which two or more readers were incorrect.

Conclusion SCOPE improves early detection of small, potentially curable, pancreatic lesions on CT by combining local segmentation and global pancreatic context. Its consistent performance across internal, external, and reader datasets supports potential use as a concurrent reader for earlier and more accurate pancreatic lesion detection.