Pediatric brain tumor classification using deep learning on MR-images with age fusion

Abstract

ABSTRACTPurposeTo implement and evaluate deep learning-based methods for the classification of pediatric brain tumors in MR data.Materials and methodsA subset of the “Children’s Brain Tumor Network” dataset was retrospectively used (n=178 subjects, female=72, male=102, NA=4, age-range [0.01, 36.49] years) with tumor types being low-grade astrocytoma (n=84), ependymoma (n=32), and medulloblastoma (n=62). T1w post-contrast (n=94 subjects), T2w (n=160 subjects), and ADC (n=66 subjects) MR sequences were used separately. Two deep-learning models were trained on transversal slices showing tumor. Joint fusion was implemented to combine image and age data, and two pre-training paradigms were utilized. Model explainability was investigated using gradient-weighted class activation mapping (Grad-CAM), and the learned feature space was visualized using principal component analysis (PCA).ResultsThe highest tumor-type classification performance was achieved when using a vision transformer model pre-trained on ImageNet and fine-tuned on ADC images with age fusion (MCC: 0.77 ± 0.14 Accuracy: 0.87 ± 0.08), followed by models trained on T2w (MCC: 0.58 ± 0.11, Accuracy: 0.73 ± 0.08) and T1w post-contrast (MCC: 0.41 ± 0.11, Accuracy: 0.62 ± 0.08) data. Age fusion marginally improved the model’s performance. Both model architectures performed similarly across the experiments, with no differences between the pre-training strategies. Grad-CAMs showed that the models’ attention focused on the brain region. PCA of the feature space showed greater separation of the tumor-type clusters when using contrastive pre-training.ConclusionClassification of pediatric brain tumors on MR-images could be accomplished using deep learning, with the top-performing model being trained on ADC data, which is used by radiologists for the clinical classification of these tumors.Key pointsThe vision transformer model pre-trained on ImageNet and fine-tuned on ADC data with age fusion achieved the highest performance, which was significantly better than models trained on T2w (second-best) and T1w-Gd data.Fusion of age information with the image data marginally improved classification, and model architecture (ResNet50 -vs -ViT) and pre-training strategies (supervised -vs -self-supervised) did not show to significantly impact models’ performance.Model explainability, by means of class activation mapping and principal component analysis of the learned feature space, show that the models use the tumor region information for classification and that the tumor type clusters are better separated when using age information.SummaryDeep learning-based classification of pediatric brain tumors can be achieved using single-sequence pre-operative MR data, showing the potential of automated decision support tools that can aid radiologists in the primary diagnosis of these tumors.