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Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma

  • Da Feng Lin
  • , Hai Lin Li
  • , Ting Liu
  • , Xiao Fei Lv
  • , Chuan Miao Xie
  • , Xiao Min Ou
  • , Jian Guan
  • , Ye Zhang
  • , Wen Bin Yan
  • , Mei Lin He
  • , Meng Yuan Mao
  • , Xun Zhao
  • , Lian Zhen Zhong
  • , Wen Hui Chen
  • , Qiu Yan Chen
  • , Hai Qiang Mai
  • , Rou Jun Peng
  • , Jie Tian
  • , Lin Quan Tang*
  • , Di Dong
  • *此作品的通讯作者
  • Sun Yat-Sen University Cancer Center
  • Collaborative Innovation Center for Cancer Medicine
  • Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy
  • Guangdong Provincial Clinical Research Center for Cancer
  • Beihang University
  • CAS - Institute of Automation
  • The First Affiliated Hospital of Sun Yat-sen University
  • Fudan University
  • Southern Medical University
  • Chinese Academy of Medical Sciences
  • University of Chinese Academy of Sciences
  • The First Affiliated Hospital of Jinan University
  • National Key Laboratory of Kidney Diseases

科研成果: 期刊稿件文章同行评审

摘要

Background: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. Methods: This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance 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 analysis. Results: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance 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 statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions: A magnetic resonance imaging–based radiomic signature predicted overall survival 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 locally recurrent nasopharyngeal carcinoma patients.

源语言英语
页(从-至)1294-1302
页数9
期刊Journal of the National Cancer Institute
116
8
DOI
出版状态已出版 - 1 8月 2024

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 3 - 良好健康与福祉
    可持续发展目标 3 良好健康与福祉

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