This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

中文翻译：

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基于 CT 扫描和全切片图像的放射病理组学模型的开发和验证，用于区分 I-II 期和 III 期胃癌

本研究旨在开发和验证人工智能放射病理学模型，使用术前 CT 扫描和术后苏木精和伊红 (HE) 染色切片来预测胃癌的病理分期（I-II 期和 III 期）。本研究共纳入 202 名病理分期已确诊的胃癌患者（训练队列：n = 141；验证队列：n = 61）。从HE切片中提取病理组织学特征，并使用逻辑回归（LR）、支持向量机（SVM）和NaiveBayes构建病理模型。通过受试者工作特征（ROC）曲线分析选择最佳病理模型。采用机器学习算法使用最佳病理模型构建放射组学模型和放射病理学模型。使用 ROC 曲线分析评估模型性能，并使用决策曲线分析 (DCA) 评估临床效用。从HE图像中总共提取了311个病理组织学特征，其中包括101个词频-逆文档频率（TF-IDF）特征和210个深度学习特征。通过降维，使用 19 个选定的病理特征构建病理模型，SVM 模型表现出优越的预测性能（AUC，训练队列：0.949；验证队列：0.777）。通过 SVM 机器算法，使用从 CT 扫描中提取的 1834 个放射组学特征中选择的 6 个特征来构建放射组学特征。同时，利用从总共2145个特征（结合放射组学和病理组学特征）降维获得的17个非零系数特征建立了放射病理组学模型。在 SVM_放射病理组学模型中观察到最好的判别能力（AUC，训练队列：0.953；验证队列：0.851），临床决策曲线分析（DCA）显示出出色的临床实用性。结合病理学和放射组学特征的放射病理组学模型在区分 I-II 期和 III 期胃癌方面表现出优越的性能。本研究基于胃癌根治性手术后手术标本的病理组织切片和术前CT图像对病理分期的预测，凸显了利用病理切片和CT图像进行病理分期研究的可行性。