Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study - Kim, Seung-seob; Seo, Hyunseok; Choi, Kihwan; Kim, Sungwon; Han, Kyunghwa; Kim, Yeun-Yoon; Seo, Nieun; Chung, Jae-Joon; Lim, Joon Seok (American Roentgen Ray Society, 2025)
BACKGROUND. Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. OBJECTIVE. The purpose of this study was 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 with 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 show 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.