Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on <scp>MR</scp> Images
Qing Yang, Ying Guo, Xiaomin Ou, Jiazhou Wang, Chaosu Hu
Abstract
BACKGROUND: Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets. PURPOSE: To develop a weakly-supervised deep-learning method to predict NPC patients' T stage without additional annotations. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n = 712) and a validation set (n = 426). FIELD STRENGTH/SEQUENCE: WI). ASSESSMENT: We used a weakly-supervised deep-learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep-learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep-learning-based T score, the progression-free survival (PFS) and overall survival (OS) were performed. STATISTICAL TESTS: The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C-indexes. The software SPSS was employed to conduct survival analysis and chi-square tests. RESULTS: The accuracy of the deep-learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C-indexes of PFS and OS from the deep-learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively. DATA CONCLUSION: This weakly-supervised deep-learning approach can perform fully automated T staging of NPC and achieve good prognostic performance. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074-1082.