Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
BMC Cancer. 2024 Mar 22;24(1):368. doi: 10.1186/s12885-024-12021-2.
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.
本研究旨在开发和验证一种人工智能放射病理模型，该模型使用术前 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 例经病理分期证实的胃癌患者（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）和朴素贝叶斯构建病理模型。通过接收者操作特征（ROC）曲线分析选择最佳病理模型。使用最佳病理模型，通过机器学习算法构建放射组学模型和放射病理模型。使用 ROC 曲线分析评估模型性能，并使用决策曲线分析（DCA）估计临床实用性。
从 HE 图像中提取了 311 个病理组织学特征，包括 101 个术语频率逆文档频率（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了一个病理模型，其中 SVM 模型表现出较好的预测性能（AUC，训练队列：0.949；验证队列：0.777）。使用 SVM 机器算法从 1834 个从 CT 扫描中提取的放射组学特征中构建了放射组学特征。同时，通过降维从总共 2145 个特征（结合放射组学和病理组学特征）中构建了一个放射病理模型。SVM_radiopathomics 模型具有最佳的判别能力（AUC，训练队列：0.953；验证队列：0.851），且临床决策曲线分析（DCA）显示出优异的临床实用性。
放射病理组学模型结合了病理和放射组学特征，在区分 I-II 期和 III 期胃癌方面表现出较好的性能。本研究基于对胃癌根治术后手术标本的病理组织切片和术前 CT 图像进行病理分期预测，突出了使用病理切片和 CT 图像进行病理分期研究的可行性。
