PANDA: Deep Learning Detects Pancreatic Cancer on Non-Contrast CT With 92.9% Sensitivity
Researchers developed PANDA, a deep learning system that detects and classifies pancreatic lesions from routine non-contrast CT scans -- a task long considered impossible. Validated across 10 centres and 20,530 consecutive patients, PANDA achieved an AUC of 0.986-0.996, outperforming radiologists by 34.1% in sensitivity while maintaining 99.9% specificity. The system could enable large-scale opportunistic screening for pancreatic cancer, a disease where early detection dramatically improves survival.
The original study
Large-scale pancreatic cancer detection via non-contrast CT and deep learning.
- Authors
- Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, et al.
- Journal
- Nature medicine
- Type
- Multicenter Study, Journal Article, Research Support, Non-U.S. Gov't
- PMID
- 37985692
Original abstract
Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.