@inproceedings{f160ee506f9e4eae9ffd7fe6f46b0e55,
title = "Radiogenomic classification of the 1p/19q status in presumed low-grade gliomas",
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.",
keywords = "1p/19q, Low grade glioma, Radiogenomics, SVM",
author = "\{Van Der Voort\}, \{Sebastian R.\} and Renske Gahrmann and \{Van Den Bent\}, \{Martin J.\} and Vincent, \{Arnaud J.P.E.\} and Niessen, \{Wiro J.\} and Marion Smits and Stefan Klein",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 ; Conference date: 15-06-2017",
year = "2017",
month = jun,
day = "15",
doi = "10.1109/ISBI.2017.7950601",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society Press",
pages = "638--641",
booktitle = "2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017",
}