Effective Pancreatic Cancer Screening on Non-contrast CT Scans via Anatomy-Aware Transformers.

Pancreatic cancer is a relatively uncommon but most deadly cancer. Screening the general asymptomatic population is not recommended due to the risk that a significant number of false positive individuals may undergo unnecessary imaging tests (e.g., multi-phase contrast-enhanced CT scans) and follow-ups, adding health care costs greatly and no clear patient benefits. In this work, we investigate the feasibility of using a single-phase non-contrast CT scan, a cheaper, simpler, and safer substituent, to detect resectable pancreatic mass and classify the detection as pancreatic ductal adenocarcinoma (PDAC) or other abnormalities (nonPDAC) or normal pancreas. This task is usually poorly performed by general radiologists or even pancreatic specialists. With pathology-confirmed mass types and knowledge transfer from contrast-enhanced CT to non-contrast CT scans as supervision, we propose a novel deep classification model with an anatomy-guided transformer. After training on a large-scale dataset including 1321 patients: 450 PDACs, 394 nonPDACs, and 477 normal, our model achieves a sensitivity of 95.2% and a specificity of 95.8% for the detection of abnormalities on the holdout testing set with 306 patients. The mean sensitivity and specificity of 11 radiologists are 79.7% and 87.6%. For the 3-class classification task, our model outperforms the mean radiologists by absolute margins of 25%, 22%, and 8% for PDAC, nonPDAC, and normal, respectively. Our work sheds light on a potential new tool for large-scale (opportunistic or designed) pancreatic cancer screening, with significantly improved accuracy, lower test risk, and cost savings.