Abstract：
BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively.
OBJECTIVE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively.
METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed.
RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891).
CONCLUSIONS: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.
OBJECTIVE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively.
METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed.
RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891).
CONCLUSIONS: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.
摘要：
背景：胃肠道间质瘤（GIST）在不同个体中具有各种恶性潜能，具有临床异质性。探索一种可靠的方法对胃GIST进行无创的术前风险分层至关重要。
目的：使用计算机断层扫描（CT）形态学的组合来建立和评估机器学习模型，影像组学，和深度学习特征来预测术前原发性胃GIST的危险分层。
方法：将193个胃GIST病变随机分为训练组，验证集,和测试集的比例为6:2:2。由两名放射科医生评估了定性和定量的CT形态学特征。肿瘤是手动分割的，然后使用PyRadiomics提取影像组学特征，并使用预训练的Resnet50从动脉期和静脉期CT图像中提取深度学习特征，分别。采用皮尔逊相关分析和递归特征消除进行特征选择。采用支持向量机来构建用于预测GIST风险分层的分类器。本研究比较了使用不同的预训练卷积神经网络（CNN）提取深度特征进行分类的模型的性能，以及从单相和双相图像建模特征的性能。动脉期,建立了静脉期和双相机器学习模型，分别,并将形态特征加入到双相机器学习模型中，构建组合模型。使用受试者工作特征（ROC）曲线来评估每个模型的功效。通过决策曲线分析(DCA)和净再分类指数(NRI)分析确定联合模型的临床应用价值。
结果：双相机器学习模型的曲线下面积（AUC）为0.876，高于动脉相模型或静脉相模型（分别为0.813、0.838）。组合模型具有比上述模型最好的预测性能,AUC为0.941(95%CI:0.887-0.974)(p=0.012,Delong检验)。DCA显示联合模型具有良好的临床应用价值，NRI为0.575（95％CI：0.357-0.891）。
结论：在这项研究中，我们建立了一个包含双相形态的组合模型，影像组学，和深度学习的特点，可用于预测胃GIST的术前风险分层。
目的：使用计算机断层扫描（CT）形态学的组合来建立和评估机器学习模型，影像组学，和深度学习特征来预测术前原发性胃GIST的危险分层。
方法：将193个胃GIST病变随机分为训练组，验证集,和测试集的比例为6:2:2。由两名放射科医生评估了定性和定量的CT形态学特征。肿瘤是手动分割的，然后使用PyRadiomics提取影像组学特征，并使用预训练的Resnet50从动脉期和静脉期CT图像中提取深度学习特征，分别。采用皮尔逊相关分析和递归特征消除进行特征选择。采用支持向量机来构建用于预测GIST风险分层的分类器。本研究比较了使用不同的预训练卷积神经网络（CNN）提取深度特征进行分类的模型的性能，以及从单相和双相图像建模特征的性能。动脉期,建立了静脉期和双相机器学习模型，分别,并将形态特征加入到双相机器学习模型中，构建组合模型。使用受试者工作特征（ROC）曲线来评估每个模型的功效。通过决策曲线分析(DCA)和净再分类指数(NRI)分析确定联合模型的临床应用价值。
结果：双相机器学习模型的曲线下面积（AUC）为0.876，高于动脉相模型或静脉相模型（分别为0.813、0.838）。组合模型具有比上述模型最好的预测性能,AUC为0.941(95%CI:0.887-0.974)(p=0.012,Delong检验)。DCA显示联合模型具有良好的临床应用价值，NRI为0.575（95％CI：0.357-0.891）。
结论：在这项研究中，我们建立了一个包含双相形态的组合模型，影像组学，和深度学习的特点，可用于预测胃GIST的术前风险分层。
