A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma

作者：Meng‐Yun Qiang, Chaofeng Li, Yuyao Sun, Ying Sun, Liang‐Ru Ke, Chuanmiao Xie, Tao Zhang, Yujian Zou, Wen‐Ze Qiu, Mingyong Gao, Yingxue Li, Xiang Li, Ze‐Jiang Zhan, Kuiyuan Liu, Xi Chen, Chixiong Liang, Qiuyan Chen, Hai‐Qiang Mai, Guotong Xie, Xiang Guo, Xing Lv · 发表于：JNCI Journal of the National Cancer Institute · 年份：2020 · DOI：10.1093/jnci/djaa149 · 被引用次数：95 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Advanced Radiotherapy Techniques

BACKGROUND: Images from magnetic resonance imaging (MRI) are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregionally advanced NPC (LA-NPC) patients to distinguish low-risk patients with LA-NPC for whom concurrent chemoradiotherapy (CCRT) is sufficient. METHODS: This multicenter, retrospective study included 3444 patients with LA-NPC from January 1, 2010, to January 31, 2017. A 3-dimensional convolutional neural network was used to learn the image features from pretreatment MRI images. An eXtreme Gradient Boosting model was trained with the MRI features and clinical data to assign an overall score to each patient. Comprehensive evaluations were implemented to assess the performance of the predictive system. We applied the overall score to distinguish high-risk patients from low-risk patients. The clinical benefit of induction chemotherapy (IC) was analyzed in each risk group by survival curves. RESULTS: We constructed a prognostic system displaying a concordance index of 0.776 (95% confidence interval [CI] = 0.746 to 0.806) for the internal validation cohort and 0.757 (95% CI = 0.695 to 0.819), 0.719 (95% CI = 0.650 to 0.789), and 0.746 (95% CI = 0.699 to 0.793) for the 3 external validation cohorts, which presented a statistically significant improvement compared with the conventional TNM staging system. In the high-risk group, patients w...