首页|Weifang People's Hospital Reports Findings in Gliomas (The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma)
Weifang People's Hospital Reports Findings in Gliomas (The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Gliomas is the subject of a report. According to news reporting out of Weifang, People's Re public of China, by NewsRx editors, research stated, "To investigate the applica tion value of support vector machine (SVM) model based on diffusion-weighted ima ging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-6 7 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%))." Our news journalists obtained a quote from the research from Weifang People's Ho spital, "All cases were divided into the training set, and validation set accord ing to the ratio of 7:3. The training set was used to select features and establ ish machine learning models. The SVM model was established with the data after f eature selection. Four single sequence models and one combined model were establ ished 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 grou p, the combined model had better predictive efficiency than single sequence mode l, 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, respectiv ely. In the IDH-1 group, the combined model was built from four selected radiomi cs features, and the AUC were 0.997 and 0.967 in the training and validation set s, respectively. The radiomics model established by DWI, DCE and APTW images cou ld be used to detect IDH-1 mutation and Ki-67 expression in glioma patients befo re surgery."
WeifangPeople's Republic of ChinaAsi aEmerging TechnologiesGliomasHealth and MedicineMachine LearningOncolo gySupport Vector MachinesVector Machines
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