Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images. Issue 4 (24th June 2020)

• Record Type: Journal Article
• Authors: Yang, Qing; Guo, Ying; Ou, Xiaomin; Wang, Jiazhou; Hu, Chaosu
• Is Part Of: Journal of magnetic resonance imaging. Volume 52:Issue 4(2020)
• Page Start: 1074
• Page End: 1082
• Publication Date: 2020-06-24
• Subjects: deep learning -- nasopharyngeal carcinoma -- staging -- magnetic resonance imaging; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27202
• Languages: English
• ISSNs: 1053-1807

Abstract

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: 1.5T, T1 ‐weighted images (T1 WI), T2 ‐weighted images (T2 WI), contrast‐enhanced T1 ‐weighted images (CE‐T1 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.