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In Diseases of the colon and rectum ; h5-index 0.0

BACKGROUND : High-resolution MRI is regarded as the best method to evaluate whether there is an involved circumferential resection margin in rectal cancer.

OBJECTIVE : We explored the application of the Faster Region-based Convolutional Neural Network to identify positive circumferential resection margins in high-resolution MRI images.

DESIGN AND SETTINGS : This was a retrospective study conducted at a single surgical unit of a public university hospital.

PATIENTS : We studied 240 patients with rectal cancer in the Affiliated Hospital of Qingdao University from July 2016 to August 2018, who were determined to have a positive circumferential resection margin and who had received a high-resolution MRI. All posttreatment cases were excluded from this study.

MAIN OUTCOME MEASURES : The Faster Region-based Convolutional Neural Network was trained by 12,258 transverse relaxation-weighted (T2-weighted imaging) images of pelvic high-resolution MRI to build an artificial intelligence platform and complete clinical tests. In this network, the proportion of positive and negative circumferential resection margin images was 1:2. In accordance with the test results of the validation group, the metrics of the receiver operating characteristic curves and the area under the curve were applied to compare the diagnostic results of the artificial intelligence platform with those of senior radiology experts.

RESULTS : In this artificial intelligence platform, the accuracy, sensitivity, and specificity of the circumferential resection margin status as determined were 0.932, 0.838, and 0.956. The area under the receiver operating characteristic curves was 0.953. The time required to automatically recognize an image was 0.2 seconds.

LIMITATIONS : This is a single-center retrospective study with limited data volume and a highly selected patient cohort.

CONCLUSIONS : In high-resolution MRI images of rectal cancer before treatment, the application of Faster Region-based Convolutional Neural Network to segment the positive circumferential resection margin has high accuracy and feasibility. See Video Abstract at

Wang Dongsheng, Xu Jihua, Zhang Zhengdong, Li Shuai, Zhang Xianxiang, Zhou Yunpeng, Zhang Xunying, Lu Yun