A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer. (30th September 2020)

• Record Type: Journal Article
• Authors: Ding, Lei; Liu, Guangwei; Zhang, Xianxiang; Liu, Shanglong; Li, Shuai; Zhang, Zhengdong; Guo, Yuting; Lu, Yun
• Is Part Of: Cancer medicine. Volume 9:Number 23(2020)
• Page Start: 8809
• Page End: 8820
• Publication Date: 2020-09-30
• Subjects: deep learning -- faster region‐based convolutional neural network -- lymph node -- metastasis -- nomogram -- rectal cancer; 616.994005
• DOI: 10.1002/cam4.3490
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: 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. Abstract : The deep learning nomogram for predicting metastatic LN status exhibits excellent discrimination of around 0.900, and good calibration and clinical utility. The deep learning nomogram for predicting LN metastasis degree also exhibits excellent discrimination of around 0.900, and calibration and clinical utility. The 2 nomograms can be jointly used as a kit for the preoperative risk prediction of various LN metastases in rectal cancer.