Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Abstract

Background: Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. Objective: To develop an artificial intelligence (AI) model to detect CRC on routine abdominopelvic CT examinations, performed without bowel preparation. Methods: This retrospective study included 3945 patients (2275 men, 1670 women; mean age, 62 years): a training set of 2662 patients from Severance Hospital with CRC who underwent routine contrast-enhanced abdominopelvic CT before treatment between January 2010 and December 2014; and internal (841 patients from Severance Hospital) and external (442 patients from Gangnam Severance Hospital) test sets of patients who underwent routine contrast-enhanced abdominopelvic CT for any indication and colonoscopy within a 2-month interval between January 2018 and June 2018. A radiologist, accessing colonoscopy reports, determined which CRCs were visible on CT and placed bounding boxes around lesions on all slices showing CRC, serving as the reference standard. A contemporary transformer-based object detection network was adapted and trained to create an AI model (https://github.com/boktae7/colorectaltumor) to automatically detect CT-visible CRC on unprocessed DICOM slices. AI performance was evaluated using alternative free-response ROC analysis, per-lesion sensitivity, and per-patient specificity; performance in the external test set was compared to that of two radiologist readers. Clinical radiology reports were also reviewed. Results: In the internal (93 CT-visible CRCs in 92 patients) and external (26 CT-visible CRCs in 26 patients) test sets, AI had AUC of 0.867 and 0.808, sensitivity of 79.6% and 80.8%, and specificity of 91.2% and 90.9%, respectively. In the external test set, the two radiologists had sensitivities of 73.1% and 80.8% (p=.74 and p>.99 vs AI) and specificities of 98.3% and 98.6% (both p<.001 vs AI); AI correctly detected five of nine CRCs missed by at least one reader. The clinical radiology reports raised suspicion for 75.9% of CRCs in the external test set. Conclusion: The findings demonstrate the AI model's utility for automated detection of CRC on routine abdominopelvic CT examinations. Clinical Impact: The AI model could help reduce the frequency of missed CRCs on routine examinations performed for reasons unrelated to CRC detection.