局部晚期鼻咽癌（LA-NPC）患者对同步放化疗的早期反应与预后密切相关。在本研究中，我们旨在使用组合模型来预测早期反应，该模型将多序列 MRI 的次区域放射组学特征与临床相关因素相结合。总共 104 名 LA-NPC 患者被随机分为训练组和测试组。比例为3：1。使用 K 均值聚类方法从肿瘤区域内的子区域中提取放射组学特征，并使用 LASSO 回归进行特征选择。建立了四种模型：放射组学模型、临床模型、基于瘤内异质性（ITH）评分的模型以及将ITH评分与临床因素相结合的组合模型。使用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估这些模型的预测性能。其中，结合ITH评分和临床因素的组合模型在测试中表现出最高的预测性能队列（AUC=0.838）。此外，基于ITH评分的模型在训练队列（AUC=0.888）和测试队列（AUC=0.833）中都显示出优异的预后价值。将ITH评分与临床因素相结合的组合模型在预测早期反应方面表现出优异的性能LA-NPC 患者同步放化疗后的结果。版权所有 © 2024 作者。由爱思唯尔有限公司出版。保留所有权利。

The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors.A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833).The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC.Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.