化学放疗组合治疗（dCRT）是局部晚期无法手术治疗的食管鳞癌（ESCC）的标准治疗选择。在进行dCRT之前评估临床结果仍然具有挑战性。本研究旨在调查基于计算机断层扫描（CT）的放射组学与基因组学结合预测ESCC患者dCRT治疗效果的预测能力。此回顾性研究纳入了118名接受dCRT的ESCC患者。这些患者随机分为训练组（n = 82）和验证组（n = 36）。从CT图像的原发肿瘤区域中提取出放射组学特征。进行最小绝对收缩和选择算子（LASSO）回归以选择最佳放射组学特征，并计算Rad score以预测训练组中的无进展生存期（PFS）。从固定在福尔马林固定和石蜡包埋的术前活检组织中提取基因组DNA。进行单变量和多变量Cox分析以确定生存预测因子进行模型开发。使用接收器操作特征曲线下面积（AUC）和C指数评估预测模型的预测性能和判别能力。 Rad score由六个放射组学特征构成，以预测PFS。多元分析表明，Rad score和同源重组修复（HRR）途径变异是独立的预后因子，与PFS相关。综合放射组学和基因组学模型的C指数在训练组中优于放射组学模型或基因组学模型（0.616 vs. 0.587或0.557），在验证组中也是如此（0.649 vs. 0.625或0.586）。 Rad score和HRR途径变异可预测ESCC患者dCRT后的PFS，组合放射组学和基因组学模型显示出最佳的预测效果。 ©2023作者。

Definitive chemoradiotherapy (dCRT) is a standard treatment option for locally advanced stage inoperable esophageal squamous cell carcinoma (ESCC). Evaluating clinical outcome prior to dCRT remains challenging. This study aimed to investigate the predictive power of computed tomography (CT)-based radiomics combined with genomics for the treatment efficacy of dCRT in ESCC patients.This retrospective study included 118 ESCC patients who received dCRT. These patients were randomly divided into training (n = 82) and validation (n = 36) groups. Radiomic features were derived from the region of the primary tumor on CT images. Least absolute shrinkage and selection operator (LASSO) regression was conducted to select optimal radiomic features, and Rad-score was calculated to predict progression-free survival (PFS) in training group. Genomic DNA was extracted from formalin-fixed and paraffin-embedded pre-treatment biopsy tissue. Univariate and multivariate Cox analyses were undertaken to identify predictors of survival for model development. The area under the receiver operating characteristic curve (AUC) and C-index were used to evaluate the predictive performance and discriminatory ability of the prediction models, respectively.The Rad-score was constructed from six radiomic features to predict PFS. Multivariate analysis demonstrated that the Rad-score and homologous recombination repair (HRR) pathway alterations were independent prognostic factors correlating with PFS. The C-index for the integrated model combining radiomics and genomics was better than that of the radiomics or genomics models in the training group (0.616 vs. 0.587 or 0.557) and the validation group (0.649 vs. 0.625 or 0.586).The Rad-score and HRR pathway alterations could predict PFS after dCRT for patients with ESCC, with the combined radiomics and genomics model demonstrating the best predictive efficacy.© 2023. The Author(s).