列线图
医学
肿瘤科
接收机工作特性
内科学
一致性
鼻咽癌
放射治疗
作者
Dafeng Lin,Hailin Li,Ting Liu,Xiaofei Lv,Chuanmiao Xie,Xiaomin Ou,Jian Guan,Ye Zhang,Wenbin Yan,Meilin He,Mengyuan Mao,Xun Zhao,Lianzhen Zhong,Wenhui Chen,Qiuyan Chen,Hai-Qiang Mai,Roujun Peng,Jie Tian,Lin‐Quan Tang,Di Dong
摘要
Abstract Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. Methods This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI