S. V. D. Voort, R. Gahrmann, M. Bent, A. Vincent, W. Niessen, M. Smits, S. Klein
{"title":"Radiogenomic classification of the 1p/19q status in presumed low-grade gliomas","authors":"S. V. D. Voort, R. Gahrmann, M. Bent, A. Vincent, W. Niessen, M. Smits, S. Klein","doi":"10.1109/ISBI.2017.7950601","DOIUrl":null,"url":null,"abstract":"1p/19q co-deletion is an important prognostic factor in low grade gliomas. However, determination of the 1p/19q status currently requires a biopsy. To overcome this, we investigate a radiogenomic classification using support vector machines to non-invasively predict the 1p/19q status from multimodal MRI data. Different approaches of predicting this status were compared: a direct approach which predicts the 1p/19q co-deletion status and an indirect approach which predicts the mutation status of 1p and 19q individually and combines these predictions to predict the 1p/19q co-deletion status. Using the indirect approach based on both the T1-weighted and T2-weighted images delivered the best result and resulted in a 95% confidence interval for the sensitivity and specificity of [0.44; 0.89] and [0.70; 1.00] respectively.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"159 1","pages":"638-641"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2017.7950601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
1p/19q co-deletion is an important prognostic factor in low grade gliomas. However, determination of the 1p/19q status currently requires a biopsy. To overcome this, we investigate a radiogenomic classification using support vector machines to non-invasively predict the 1p/19q status from multimodal MRI data. Different approaches of predicting this status were compared: a direct approach which predicts the 1p/19q co-deletion status and an indirect approach which predicts the mutation status of 1p and 19q individually and combines these predictions to predict the 1p/19q co-deletion status. Using the indirect approach based on both the T1-weighted and T2-weighted images delivered the best result and resulted in a 95% confidence interval for the sensitivity and specificity of [0.44; 0.89] and [0.70; 1.00] respectively.