Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Shandong Provincial Hospital Affiliated to Cheeloo College of Medicine of Shandong University, Jinan, China.
BMC Med Imaging. 2024 Sep 16;24(1):244. doi: 10.1186/s12880-024-01414-1.
To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma.
The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation.
Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively.
The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.
探讨基于磁共振扩散加权成像（DWI）、动态对比增强（DCE）和酰胺质子转移加权成像（APTW）的支持向量机（SVM）模型在预测胶质瘤异柠檬酸脱氢酶 1（IDH-1）突变和 Ki-67 表达中的应用价值。
回顾性分析经病理证实的 309 例脑胶质瘤患者的 DWI、DCE 和 APTW 图像，分为 IDH-1 组（IDH-1(+)组和 IDH-1(-)组）和 Ki-67 组（低表达组（Ki-67≤10%）和高表达组（Ki-67＞10%））。所有病例按 7：3 的比例分为训练集和验证集。利用训练集选择特征并建立机器学习模型，利用特征选择后的数据建立 SVM 模型。在 IDH-1 组和 Ki-67 组中，分别建立 4 个单序列模型和 1 个组合模型。利用受试者工作特征（ROC）曲线评估模型的诊断效能，利用验证集数据进一步验证。
在 IDH-1 组和 Ki-67 组中，组合模型的预测效能均优于单序列模型，尽管单序列模型具有更好的预测效能。在 Ki-67 组中，组合模型由 6 个选定的放射组学特征构建，在训练集和验证集中的 AUC 分别为 0.965 和 0.931。在 IDH-1 组中，组合模型由 4 个选定的放射组学特征构建，在训练集和验证集中的 AUC 分别为 0.997 和 0.967。
基于 DWI、DCE 和 APTW 图像的放射组学模型可用于术前检测脑胶质瘤患者的 IDH-1 突变和 Ki-67 表达。基于组合序列的放射组学模型的预测性能优于单序列模型。
