Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomography

2026-01-29

AI Summary

This study addresses the critical challenge of delayed diagnosis in pancreatic ductal adenocarcinoma (PDAC), which often arises due to the occult nature of early lesions. To overcome this, we developed ePAI, a deep learning–based artificial intelligence system trained on multicenter computed tomography (CT) imaging data to automatically detect early and even pre-clinical PDAC lesions. In internal testing, ePAI achieved an AUC of 0.939–0.999, with 95.3% sensitivity and 98.7% specificity. External validation demonstrated 91.5% sensitivity and 88.0% specificity. Notably, ePAI retrospectively identified 75 out of 159 cases a median of 347 days earlier than clinical diagnosis, significantly outperforming a panel of 30 experienced radiologists and achieving high-accuracy detection of lesions missed up to 3–36 months prior.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Learning to Search
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective reviews of prediagnostic CT scans, when conducted by expert radiologists aware that the patient later developed PDAC, frequently reveal lesions that were previously overlooked. To help detecting these lesions earlier, we developed an automated system named ePAI (early Pancreatic cancer detection with Artificial Intelligence). It was trained on data from 1,598 patients from a single medical center. In the internal test involving 1,009 patients, ePAI achieved an area under the receiver operating characteristic curve (AUC) of 0.939-0.999, a sensitivity of 95.3%, and a specificity of 98.7% for detecting small PDAC less than 2 cm in diameter, precisely localizing PDAC as small as 2 mm. In an external test involving 7,158 patients across 6 centers, ePAI achieved an AUC of 0.918-0.945, a sensitivity of 91.5%, and a specificity of 88.0%, precisely localizing PDAC as small as 5 mm. Importantly, ePAI detected PDACs on prediagnostic CT scans obtained 3 to 36 months before clinical diagnosis that had originally been overlooked by radiologists. It successfully detected and localized PDACs in 75 of 159 patients, with a median lead time of 347 days before clinical diagnosis. Our multi-reader study showed that ePAI significantly outperformed 30 board-certified radiologists by 50.3% (P<0.05) in sensitivity while maintaining a comparable specificity of 95.4% in detecting PDACs early and prediagnostic. These findings suggest its potential of ePAI as an assistive tool to improve early detection of pancreatic cancer.

Problem

• Pancreatic cancer
• Early detection
• Prediagnostic detection
• Computed tomography
• Missed lesions

Innovation

• early detection
• artificial intelligence
• pancreatic cancer
• prediagnostic CT
• deep learning