Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0 | |
Zhong, Lianzhen1; Fang, Xueliang2; Dong, Di1; Peng, Hao2; Fang, Mengjie1; Huang, Chenglong2; He, Bingxi1; Lin, Li2; Ma, Jun2; Tang, Linglong2; Tian, Jie1 | |
发表期刊 | Radiotherapy and Oncology |
2020 | |
卷号 | 151期号:1页码:1-9 |
文章类型 | article |
摘要 | Purpose: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT). Methods: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT+CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (Cindex) and the Kaplan-Meier estimator. Results: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695-0.731, all p < 0.001) and test (C-index: 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001). Conclusions: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC. |
关键词 | nasopharyngeal carcinoma |
DOI | 10.1016/j.radonc.2020.06.050 |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000589794300002 |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40685 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Ma, Jun; Tang, Linglong; Tian, Jie |
作者单位 | 1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, P. R. China 2.State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhong, Lianzhen,Fang, Xueliang,Dong, Di,et al. A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0[J]. Radiotherapy and Oncology,2020,151(1):1-9. |
APA | Zhong, Lianzhen.,Fang, Xueliang.,Dong, Di.,Peng, Hao.,Fang, Mengjie.,...&Tian, Jie.(2020).A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0.Radiotherapy and Oncology,151(1),1-9. |
MLA | Zhong, Lianzhen,et al."A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0".Radiotherapy and Oncology 151.1(2020):1-9. |
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