Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine

Frontiers in Neuroscience
Yu HanLin-Feng Yan

Abstract

To compare the efficacies of univariate and radiomics analyses of amide proton transfer weighted (APTW) imaging in predicting isocitrate dehydrogenase 1 (IDH1) mutation of grade II/III gliomas. Fifty-nine grade II/III glioma patients with known IDH1 mutation status were prospectively included (IDH1 wild type, 16; IDH1 mutation, 43). A total of 1044 quantitative radiomics features were extracted from APTW images. The efficacies of univariate and radiomics analyses in predicting IDH1 mutation were compared. Feature values were compared between two groups with independent t-test and receiver operating characteristic (ROC) analysis was applied to evaluate the predicting efficacy of each feature. Cases were randomly assigned to either the training (n = 49) or test cohort (n = 10) for the radiomics analysis. Support vector machine with recursive feature elimination (SVM-RFE) was adopted to select the optimal feature subset. The adverse impact of the imbalance dataset in the training cohort was solved by synthetic minority oversampling technique (SMOTE). Subsequently, the performance of SVM model was assessed on both training and test cohort. As for univariate analysis, 18 features were significantly different between IDH1 wild-type a...Continue Reading

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Citations

Nov 28, 2020·AJNR. American Journal of Neuroradiology·A P BhandariA Lasocki
Jul 3, 2021·Cancers·Agustina La Greca Saint-EstevenStephanie Tanadini-Lang

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Methods Mentioned

BETA
surgical resection
imaging technique
feature extraction

Clinical Trials Mentioned

NCT03102112

Software Mentioned

WEKA
ITK
Waikato Environment for Knowledge Analysis ( WEKA
SPSS
Analysis
- Kinetics
R
SNAP

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