Automated glioma multiclass tumor classification

Abstract

Gliomas are primary brain tumors that arise from neural stem cells or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and some molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of the images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. Moreover, the challenge of the study was given by small sample size of human leukocyte antigen tissue microarrays dataset (HLA-TMA) - total 204 images from 5 classes - and imbalanced data distribution. This has been addressed by images augmentation of the underrepresented classes, as already shown in a similar study about predicting mutations from glioma biopsies.1 For this glioma multiclass classification task, the architecture of residual neural network has been adapted. The best model produced an accuracy score of 0.7727, and the mean accuracy of the cross-validation iterations was the value of 0.7248 on the validation set. This promising approach can be used as an additional diagnostic tool to improve assessment during intra-operative examination or sub-typing tissues for treatment selection, despite the challenges presented by the difficult dataset.