1. Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study
- Author
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Ting Liu, Di Dong, Xun Zhao, Xiao-Min Ou, Jun-Lin Yi, Jian Guan, Ye Zhang, Lv Xiao-Fei, Chuan-Miao Xie, Dong-Hua Luo, Rui Sun, Qiu-Yan Chen, Lv Xing, Shan-Shan Guo, Li-Ting Liu, Da-Feng Lin, Yan-Zhou Chen, Jie-Yi Lin, Mei-Juan Luo, Wen-Bin Yan, Mei-Lin He, Meng-Yuan Mao, Man-Yi Zhu, Wen-Hui Chen, Bo-Wen Shen, Shi-Qian Wang, Hai-Lin Li, Lian-Zhen Zhong, Chao-Su Hu, De-Hua Wu, Hai-Qiang Mai, Jie Tian, and Lin-Quan Tang
- Subjects
Recurrent nasopharyngeal carcinoma ,Re-radiotherapy ,Nasopharyngeal necrosis ,Radiomics ,Medicine - Abstract
Abstract Background Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. Methods This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. Results The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P
- Published
- 2023
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