BackgroundIsocitrate 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.MethodsMulti-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. A 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.ResultsT2-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.ConclusionWe demonstrate high IDH classification accuracy using only T2-weighted MRI. This represents an important milestone towards clinical translation.Keypoints – 1IDH status is an important prognostic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of IDH status 3. The deep-learning networks utilizes only T2 weighted MR images to predict IDH status thereby facilitating clinical translation.IMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation status as a marker for therapy and prognosis. The mutated form of the gene confers a better prognosis and treatment response than gliomas with the non-mutated or wild-type form. Currently, the only reliable way to determine IDH mutation status is to obtain glioma tissue either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine IDH mutation status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published methods. The high IDH classification accuracy of our T2w image only network (T2-net) marks an important milestone towards clinical translation. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

BACKGROUND AND PURPOSE: O 6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only. MATERIALS AND METHODS: Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Threefold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy. RESULTS: The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008]. CONCLUSIONS: We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.

Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented.The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm (xgboost) with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrasted this method can achieve excellent performance with DICE scores of 0.898, 0.784, 0.779 for whole tumor, tumor core and enhancing tumor respectively and accuracy 34.5 % for survuval prediction. * -Equal Contribution