A Novel Fully Automated Mri Based Deep Learning Method for Classification of Idh Mutation Status in Brain Gliomas.
BACKGROUND Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI based, voxel wise deep learning IDH classification network using T2 weighted (T2w) MR images and compare its performance to a multi contrast network. METHODS Multi parametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH mutated, 120 IDH wild type) from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) Two separate networks were developed including a T2w image only network (T2 net) and a multi contrast (T2w, FLAIR, and T1 post contrast) network (TS net) to perform IDH classification and simultaneous single label tumor segmentation. The networks were trained using 3D Dense UNets. Three fold cross validation was performed to generalize the networks' performance. ROC analysis was also performed. Dice scores were computed to determine tumor segmentation accuracy. RESULTS T2 net demonstrated a mean cross validation accuracy of 97.14% 0.04 in predicting IDH mutation status, with a sensitivity of 0.97 0.03, specificity of 0.98 0.01, and an AUC of 0.98 0.01. TS net achieved a mean cross validation accuracy of 97.12% 0.09, with a sensitivity of 0.98 0.02, specificity of 0.97 0.001, and an AUC of 0.99 0.01. The mean whole tumor segmentation Dice scores were 0.85 0.009 for T2 net and 0.89 0.006 for TS net. CONCLUSION We demonstrate high IDH classification accuracy using only T2 weighted MR images. This represents an important milestone towards clinical translation.
Neuro oncology