Effective Opportunistic Esophageal Cancer Screening Using Noncontrast CT Imaging

Esophageal cancer is the second most deadly cancer. Early detection of resectable/curable esophageal cancers has a great potential to reduce mortality, but no guideline-recommended screening test is available. Although some screening methods have been developed, they are expensive, might be difficult to apply to the general population, and often fail to achieve satisfactory sensitivity for identifying early-stage cancers. In this work, we investigate the feasibility of esophageal tumor detection and classification (cancer or benign) on the noncontrast CT scan, which could potentially be used for opportunistic cancer screening. To capture the global context, a novel position-sensitive self-attention is proposed to augment nnUNet with non-local interactions. Our model achieves a sensitivity of 93.0% and specificity of 97.5% for the detection of esophageal tumors on a holdout testing set with 180 patients. In comparison, the mean sensitivity and specificity of four doctors are 75.0% and 83.8%, respectively. For the classification task, our model outperforms the mean doctors by absolute margins of 17%, 31%, and 14% for cancer, benign tumor, and normal, respectively. Compared with established state-of-the-art esophageal cancer screening methods, e.g., blood testing and endoscopy AI system, our method has comparable performance and is even more sensitive for early-stage cancer and benign tumor. Our proposed method is a novel, non-invasive, low-cost, and highly accurate tool for opportunistic screening of esophageal cancer.