Colorectal cancer detection using noncontrast CT and deep learning: a multicenter and international cohort study.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer deaths, with early screening vital to reduce mortality. While methods such as colonoscopy and computed tomography (CT) colonography are available, they face challenges such as bowel preparation, invasiveness, and low adherence. We aimed to develop COCA (COlorectal Cancer detection with AI), a novel, noninvasive, cost-effective, and scalable method for CRC screening using noncontrast CT scans. PATIENTS AND METHODS: This retrospective, multicenter, and international study included 1321 CRC patients and 1357 normal controls from two centers to develop COCA. We enhanced the CRC detection capabilities of COCA by employing a joint lesion segmentation and classification architecture, optimized with mixed-supervised learning. For validation, we gathered abdominal and pelvic CT data from four external centers and chest CT data from four centers. A reader study involving 10 radiologists with varying levels of experience evaluated diagnostic performance on noncontrast CT first without COCA assistance and then with it. Additionally, we evaluated both the initial and iteratively improved versions of COCA in two real-world, multi-scenario cohorts comprising 27 433 consecutive patients. RESULTS: In a multicenter and international validation involving 2053 patients across six centers, COCA demonstrated an area under the curve ranging from 0.967 to 0.996 for CRC detection. COCA improved CRC detection sensitivity by 20.4% and specificity by 5.4% compared with radiologists. In the first real-world multi-scenario validation with 9014 consecutive patients, COCA achieved a sensitivity of 88.2% and specificity of 99.5% for CRC detection. In the second external real-world validation involving 18 419 consecutive patients, COCA maintained a sensitivity of 86.6% and specificity of 99.8%, with a positive predictive value of 63.4%. CONCLUSIONS: COCA demonstrated robust performance across various clinical scenarios, including physical exams, emergency departments, outpatient, and inpatient settings, effectively preventing missed CRC diagnoses. These findings suggest that COCA could serve as a potential tool for large-scale opportunistic CRC screening.