To pre-operatively and non-invasively predict 1p/19q co-deletion in grade II and III (lower-grade) glioma based on a radiomics method using magnetic resonance imaging (MRI). We obtained 105 patients pathologically diagnosed with lower-grade glioma. We extracted 647 MRI-based features from T2-weighted images and selected discriminative features by lasso logistic regression approaches on the training cohort (n=69). Radiomics, clinical, and combined models were constructed separately to verify the predictive performance of the radiomics signature. The predictability of the three models were validated on a time-independent validation cohort (n = 36). Finally, 7 discriminative radiomic features were used constructed radiomics signature, which demonstrated satisfied performance on both the training and validation cohorts with AUCs of 0.822 and 0.731, respectively. Particularly, the combined model incorporating the radiomics signature and the clinic-radiological factors achieved the best discriminative capability with AUCs of 0.911 and 0.866 for training and validation cohorts, respectively.

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502B (13 March 2019); doi: 10.1117/12.2511501

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)