To distinguish metastatic and non-metastatic mesenteric lymph nodes(LNs)in rectal cancer using deep learning based on high-resolution T2WI.

A total of 166 patients with rectal space-occupying lesions were enrolled in Shandong Provincial Qianfoshan Hospital from June 2016 to April 2021.All patients underwent 3.0T magnetic resonance scanning, including high-resolution T2WI(HR-T2WI)sequence, and underwent total mesorectal excision(TME)within two weeks.The LNs within the mesentery were located and characterized by radiologist and pathologist.The LNs were randomly divided into training set and test set according to the ratio of 7∶3.HR-T2WI images were extracted from the enrolled patients, and the mesorectal LNs were labeled to get region of interest(ROI). Random flipping and adding random noise were used to increase the robustness.A convolutional neural network(CNN)model consisting of five convolution modules and two fully connected layers was established, and was trained and tested respectively.The evaluation indexes included ROC curve, AUC, accuracy, sensitivity and specificity.

A total of 604 LNs(298 benign and 306 malignant)were finally obtained.After retrograde deep learning CNN model training of the LNs in the training set(215 malignant+ 205 benign)and the LNs in the test set(91 malignant+ 93 benign), the AUCs of the training set and the test set were 0.910 and 0.820 respectively.The accuracy, sensitivity and specificity of the test set were 0.725, 0.698 and 0.752 respectively.

The deep learning method based on HR-T2WI sequence can be used to identify the status of mesorectal LNs in rectal cancer.