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.
Original language | English |
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Title of host publication | 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 |
Publisher | IEEE Computer Society |
Pages | 638-641 |
Number of pages | 4 |
ISBN (Electronic) | 9781509011711 |
DOIs | |
Publication status | Published - 15 Jun 2017 |
Event | 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: 18 Apr 2017 → 21 Apr 2017 |
Publication series
Series | Proceedings - International Symposium on Biomedical Imaging |
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ISSN | 1945-7928 |
Conference
Conference | 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 |
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Country/Territory | Australia |
City | Melbourne |
Period | 18/04/17 → 21/04/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Research programs
- EMC NIHES-03-30-03