鼻咽癌
医学
阶段(地层学)
深度学习
肿瘤科
化疗
内科学
远处转移
队列
回顾性队列研究
转移
放射治疗
人工智能
癌症
计算机科学
生物
古生物学
作者
Yujun Hu,Lin Zhang,Youping Xiao,Tianzhu Lu,Qiaojuan Guo,Shaojun Lin,Lan Liu,Yunbin Chen,Zilu Huang,Ya Liu,Yong Su,Lizhi Liu,Xiaochang Gong,Jianji Pan,Li Jingao,Yunfei Xia
出处
期刊:iScience
[Cell Press]
日期:2023-05-19
卷期号:26 (6): 106932-106932
被引量:7
标识
DOI:10.1016/j.isci.2023.106932
摘要
Chemotherapy remains controversial for stage II nasopharyngeal carcinoma because of its considerable prognostic heterogeneity. We aimed to develop an MRI-based deep learning model for predicting distant metastasis and assessing chemotherapy efficacy in stage II nasopharyngeal carcinoma. This multicenter retrospective study enrolled 1072 patients from three Chinese centers for training (Center 1, n = 575) and external validation (Centers 2 and 3, n = 497). The deep learning model significantly predicted the risk of distant metastases for stage II nasopharyngeal carcinoma and was validated in the external validation cohort. In addition, the deep learning model outperformed the clinical and radiomics models in terms of predictive performance. Furthermore, the deep learning model facilitates the identification of high-risk patients who could benefit from chemotherapy, providing useful additional information for individualized treatment decisions.
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