治疗前准确预测化疗的病理反应对于选择合适的治疗组、制定个体化治疗方案、提高胃癌（GC）患者的生存率具有重要意义。我们回顾性入组了151例术前化疗后确诊为GC的患者。 2015年1月至2023年6月在青岛大学附属医院进行的手术切除。每位患者均可获得预处理增强的计算机技术图像和病理苏木精和伊红染色切片的全玻片图像。提取图像特征并用于构建整体放射病理学机器学习模型。此外，还结合影像特征和临床特征开发了列线图。总共从训练队列中的 106 名患者中提取了 962 个放射组学特征和 999 个病理组学特征。使用 13 个放射组学和 5 个病理组学特征构建了融合放射病理组学模型。与单组学模型相比，融合模型表现出良好的性能，验证队列中的曲线下面积 (AUC) 为 0.789。此外，基于放射病理学特征和Borrmann类型，开发了组合放射病理组学列线图（RPN），这是根据肿瘤生长模式和大体形态对晚期GC进行分类的方法。 RPN 在训练组 (AUC 0.880) 和验证组 (AUC 0.797) 中显示出卓越的预测性能。决策曲线分析表明RPN可以为GC患者提供良好的临床获益。RPN能够高精度预测术前化疗的病理反应，为GC的个性化治疗提供了一种新的工具。版权所有©2024 The Association of大学放射科医生。由爱思唯尔公司出版。保留所有权利。

Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of patients with gastric cancer (GC).We retrospectively enrolled 151 patients diagnosed with GC who underwent preoperative chemotherapy and surgical resection at the Affiliated Hospital of Qingdao University between January 2015 and June 2023. Both pretreatment-enhanced computer technology images and whole slide images of pathological hematoxylin and eosin-stained sections were available for each patient. The image features were extracted and used to construct an ensemble radiopathomics machine learning model. In addition, a nomogram was developed by combining the imaging features and clinical characteristics.In total, 962 radiomics and 999 pathomics signatures were extracted from 106 patients in the training cohort. A fusion radiopathomics model was constructed using 13 radiomics and 5 pathomics signatures. The fusion model showed favorable performance compared to single-omics models, with an area under the curve (AUC) of 0.789 in the validation cohort. Moreover, a combined radiopathomics nomogram (RPN) was developed based on radiopathomics features and the Borrmann type, which is a classification method for advanced GC according to tumor growth pattern and gross morphology. The RPN showed superior predictive performance in the training (AUC 0.880) and validation cohorts (AUC 0.797). The decision curve analysis showed that RPN could provide favorable clinical benefits to patients with GC.RPN was able to predict the pathological response to preoperative chemotherapy with high accuracy, and therefore provides a novel tool for personalized treatment of GC.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.