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.

中文翻译：

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与肿瘤免疫异质性相关的放射组学特征可预测局部复发鼻咽癌的生存率

背景 传统临床指标对局部复发鼻咽癌（lrNPC）的预后价值由于无法反映瘤内异质性而受到限制。我们的目标是开发放射组学特征来揭示肿瘤免疫异质性并预测 lrNPC 的生存率。方法 这项多中心回顾性研究纳入了 921 名 lrNPC 患者。基于治疗前 MRI 特征的机器学习特征和列线图被开发用于预测训练队列中的总生存期 (OS)，并在两个独立队列中进行验证。构建临床列线图和综合列线图进行比较。通过一致性指数（C 指数）和受试者工作特征曲线分析来评估列线图性能。因此，患者被分为危险组。通过RNA测序（RNA-seq）分析探讨了该特征的生物学特征和免疫浸润。结果 机器学习特征和列线图表现出与临床列线图相当的预后能力，在训练组、内部验证组和外部验证组中分别实现了 0.729、0.718 和 0.731 的 C 指数。特征和临床变量的整合显着提高了预测性能。所提出的签名有效地区分了具有显着不同 OS 率的风险组之间的患者。亚组分析表明建议对低风险患者进行局部挽救治疗。探索性 RNA-seq 分析揭示了风险组之间干扰素反应和淋巴细胞浸润的差异。结论 基于 MRI 的放射组学特征可以更准确地预测 OS。所提出的与肿瘤免疫异质性相关的特征可能作为促进 lrNPC 患者预后分层和指导个体化管理的有价值的工具。