Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences

Abstract

Background and Purpose. The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual Network (Shuffle-ResNet) to predict IDH1 gene mutation status of the low grade glioma (LGG) tumors from multicenter anatomical magnetic resonance imaging (MRI) sequences including T2-w, T2-FLAIR, T1-w, and T1-Gd. Methods and Materials. We used 105 patient's dataset available in The Cancer Genome Atlas LGG project where we split them into training and testing datasets. We implemented a random image patch extractor to leverage tumor heterogeneity where about half a million image patches were extracted. RGB dataset were created from image concatenation. We used random channel-shuffle layer in the ResNet architecture to improve the generalization, and, also, a 3-fold cross validation to generalize the network's performance. The early stopping algorithm and learning rate scheduler were employed to automatically halt the training. Results. The early stopping algorithm terminated the training after 131, 106, and 96 epochs in fold 1, 2, and 3. The accuracy and area under the curve (AUC) of the validation dataset were 81.29% (95% CI (79.87, 82.72)) and 0.96 (95% CI (0.92, 0.98)) when we concatenated T2-FLAIR, T1-Gd, and T2-w to produce an RGB dataset. The accuracy and AUC values of the test dataset were 85.7% and 0.943. Conclusions. Our Shuffle-ResNet could predict IDH1 gene mutation status using multicenter MRI. However, its clinical application requires more investigation.