开发和验证利用 CT 数据预测胃癌 (GC) 患者神经周围浸润 (PNI) 和生存的放射组学列线图。对来自两个机构的 408 名 GC 患者进行回顾性分析：来自机构 I 的 288 名患者按 7:3 分为一组训练集 (n = 203) 和测试集 (n = 85)；来自机构 II 的 120 名患者作为外部验证集。从 CT 图像中提取并筛选放射组学特征。构建独立的放射组学、临床和组合模型来预测 PNI。分别使用曲线下面积 (AUC)、校准曲线、决策曲线分析和 Kaplan-Meier 曲线评估模型判别、校准、临床实用性和预后意义。最终分析中包括 15 个放射组学特征和 3 个临床因素。训练、测试和外部验证集中放射组学模型的 AUC 分别为 0.843 (95% CI: 0.788-0.897)、0.831 (95% CI: 0.741-0.920) 和 0.802 (95% CI: 0.722-0.882) ， 分别。通过将重要的临床因素与放射组学特征相结合，开发了列线图。训练、测试和外部验证集中列线图的 AUC 分别为 0.872 (95% CI: 0.823-0.921)、0.862 (95% CI: 0.780-0.944) 和 0.837 (95% CI: 0.767-0.908)，分别。生存分析显示，列线图可以有效地对患者的无复发生存进行分层（风险比：4.329；95% CI：3.159-5.934；P < 0.001）。放射组学衍生的列线图为预测 GC 中的 PNI 提供了一种有前途的工具，并持有显着的预后影响。列线图作为确定 PNI 状态的非侵入性生物标志物。列线图的预测性能优于临床模型（P < 0.05）。此外，按列线图分层的高危组患者的 RFS 显着缩短 (P < 0.05)。版权所有 © 2024 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 develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.