接受新辅助免疫治疗（NIT）的可切除食管鳞状细胞癌（ESCC）患者表现出不同的治疗反应。本研究的目的是建立并验证基于增强计算机断层扫描 (CT) 并结合临床数据的放射组学，以预测 ESCC 患者对 NIT 的主要病理反应。 这项回顾性研究包括 82 名 ESCC 患者，他们被随机分为训练组 (n = 57) 和验证组 (n = 25)。放射组学特征源自治疗前获得的增强 CT 图像中的肿瘤区域。经过特征缩减和筛选后，建立了放射组学。进行逻辑回归分析以选择临床变量。构建了整合放射组学和临床数据的预测模型并以列线图的形式呈现。采用曲线下面积（AUC）评估模型的预测能力，并进行决策曲线分析（DCA）和校准曲线来测试模型的应用。识别出1个临床数据（放疗）和10个放射组学特征，并进行分析。应用于预测模型。放射组学与临床数据相结合可以实现出色的预测性能，训练组和验证组的 AUC 值分别为 0.93（95% CI 0.87-0.99）和 0.85（95% CI 0.69-1.00）。 DCA和校准曲线证明了该模型良好的临床可行性和实用性。基于增强CT图像的放射组学可以以高精度和稳健性预测ESCC患者对NIT的反应。开发的预测模型为在开始治疗前评估治疗效果提供了宝贵的工具，从而为患者提供个体化治疗方案。版权所有 © 2024 Wang、Tang、Zhong、Wang、Feng、Zhang 和 Liu。

Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients.This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models.One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model.Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.Copyright © 2024 Wang, Tang, Zhong, Wang, Feng, Zhang and Liu.