通过多中心研究探讨计算机断层扫描（CT）放射组学列线图在术前预测食管鳞状细胞癌（ESCC）神经周围侵犯（PNI）中的价值。我们回顾性收集了360例明确PNI状态的ESCC患者的术后病理资料。 131 名 PNI 阳性和 229 名 PNI 阴性）来自两个中心。从动脉期CT图像中提取放射组学特征，并使用最小绝对收缩和选择算子以及逻辑回归算法筛选有价值的特征，以识别PNI状态并计算放射组学评分（Rad-score）。通过整合 Rad 评分和临床危险因素建立放射组学列线图。使用受试者工作特征曲线来评估模型性能，并使用决策曲线分析来评估放射组学列线图在训练、内部验证和外部验证集中的预测性能。从全体积肿瘤中提取了 20 个放射组学特征放射组学列线图结合放射组学特征和临床危险因素构建模型，在预测食管鳞癌患者的 PNI 状态方面优于临床和放射组学模型。训练、内部验证和外部验证集中放射组学列线图的曲线下面积分别为 0.856 (0.794-0.918)、0.832 (0.742-0.922) 和 0.803 (0.709-0.898)。对CT具有优异的预测能力；它可以无创地预测食管鳞癌患者术前PNI状态，为术前决策提供依据。版权所有©2023大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study.We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets.Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794-0.918), 0.832 (0.742-0.922), and 0.803 (0.709-0.898), respectively.The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.