Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter...

Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning.

387 cases of primary glioma from three hospitals were collected, along with their T1 contrast-enhanced and T2-weighted MR sequences, pathological reports and clinical histories. The training set consisted of 264 patients, 82 patients composed the test set, and 41 patients were used as the validation set for hyperparameter tuning and optimal model selection. All groups were sourced from different centers. Through radiomics, deep learning, habitat analysis and combined analysis, we extracted imaging features separately and jointly modeled them with clinical features. We identified the optimal models for predicting glioma grades, Ki67 expression levels, P53 mutation and IDH1 mutation.

Using a LightGBM model with DenseNet161 features based on habitat subregions, the best tumor grade prediction model was achieved. A LightGBM model with ResNet50 features based on habitat subregions yielded the best Ki67 expression level prediction model. An SVM model with Radiomics and Inception_v3 features provided the best prediction of P53 mutation. The best model for predicting IDH1 mutation was achieved by an MLP model with Radiomics features based on habitat subregions. Clinical features might be potentially helpful for the prediction with relatively weak evidence.

Habitat+Deep Learning feature extraction methods were optimal for predicting grades and Ki67 levels. Deep Learning is optimal for predicting P53 mutation, while the combination of Habitat+ Radiomics models yielded the best prediction for IDH1 mutation.