In Cancer medicine
BACKGROUND : Preoperative diagnoses of metastatic lymph nodes (LNs) by the most advanced deep learning technology of Faster Region-based Convolutional Neural Network (Faster R-CNN) have not yet been reported.
MATERIALS AND METHODS : In total, 545 patients with pathologically confirmed rectal cancer between January 2016 and March 2019 were included and were randomly allocated with a split ratio of 2:1 to the training and validation sets, respectively. The MRI images for metastatic LNs were evaluated by Faster R-CNN. Multivariate regression analyses were used to develop the predictive models. Faster R-CNN nomograms were constructed based on the multivariate analyses in the training sets and were validated in the validation sets.
RESULTS : The Faster R-CNN nomogram for predicting metastatic LN status contained predictors of age, metastatic LNs by Faster R-CNN and differentiation degrees of tumors, with areas under the curves (AUCs) of 0.862 (95% CI: 0.816-0.909) and 0.920 (95% CI: 0.876-0.964) in the training and validation sets, respectively. The Faster R-CNN nomogram for predicting LN metastasis degree contained predictors of metastatic LNs by Faster R-CNN and differentiation degrees of tumors, with AUCs of 0.859 (95% CI: 0.804-0.913) and 0.886 (95% CI: 0.822-0.950) in the training and validation sets, respectively. Calibration plots and decision curve analyses demonstrated good calibrations and clinical utilities. The two nomograms were used jointly as a kit for predicting metastatic LNs.
CONCLUSION : The Faster R-CNN nomogram kit exhibits excellent performance in discrimination, calibration, and clinical utility and is convenient and reliable for predicting metastatic LNs preoperatively.
CLINICAL TRIAL REGISTRATION : ChiCTR-DDD-17013842.
Ding Lei, Liu Guangwei, Zhang Xianxiang, Liu Shanglong, Li Shuai, Zhang Zhengdong, Guo Yuting, Lu Yun
deep learning, faster region-based convolutional neural network, lymph node, metastasis, nomogram, rectal cancer