Pediatric brain tumor classification using deep learning on MR-images from the children’s brain tumor network

Abstract

Brain tumors are among the leading causes of cancer deaths in children. Initial diagnosis based on MR images can be a challenging task for radiologists, depending on the tumor type and location. Deep learning methods could support the diagnosis by predicting the tumor type. A subset (181 subjects) of the data from "Children's Brain Tumor Network" (CBTN) was used, including infratentorial and supratentorial tumors, with the main tumor types being low-grade astrocytomas, ependymomas, and medulloblastomas. T1w-Gd, T2-w, and ADC MR sequences were used separately. Classification was performed on 2D MR images using four different off-the-shelf deep learning models and a custom-designed shallow network all pre-trained on adult MR images. Joint fusion was implemented to combine image and age data, and tumor type prediction was computed volume-wise. Matthew's correlation coefficient (MCC), accuracy, and F1 scores were used to assess the models' performance. Model explainability, using gradient-weighted class activation mapping (Grad-CAM), was implemented and the network's attention on the tumor region was quantified. The shallow custom network resulted in the highest classification performance when trained on T2-w or ADC MR images fused with age information, when considering infratentorial tumors only (MCC: 0.71 for ADC and 0.57 for T2-w), and both infra- and supratentorial tumors (MCC: 0.70 for ADC and 0.57 for T2-w). Classification of pediatric brain tumors on MR images could be accomplished using deep learning, and the fusion of age information improved model performance.