• 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.
• Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
• Bmc Med. 2023 Nov 27; 21 (1): 464464.

BackgroundPost-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.MethodsThis 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.ResultsThe 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 < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.ConclusionsWe present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.© 2023. The Author(s).

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