Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study

Abstract

The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. MRI scans from consecutive patients with histologically confirmed HGG (grade 3 or 4) were reviewed. Scans for which recurrence or TRC was queried were followed up to determine whether the cases indicated recurrence/progression or TRC. Identified cases were randomly split into training and testing sets (80%/20%). Following development on the training set, classification experiments using convolutional neural networks (CNN) were then conducted using models based on each of diffusion weighted imaging (DWI – isotropic diffusion map), apparent diffusion coefficient (ADC), FLAIR and post-contrast T1 sequences. The sequence that achieved the highest accuracy on the test set was then used to develop DL models in which multiple sequences were combined. MRI scans from 55 patients were included in the study (70.1% progression/recurrence). 54.5% of the randomly allocated test set had progression/recurrence. Based upon DWI sequences the CNN achieved an accuracy of 0.73 (F1 score = 0.67). The model based on the DWI+FLAIR sequences in combination achieved an accuracy of 0.82 (F1 score = 0.86). The results of this study support similar studies that have shown that machine learning, in particular DL, may be useful in distinguishing progression/recurrence from TRC. Further studies examining the accuracy of DL models, including magnetic resonance perfusion (MRP) and magnetic resonance spectroscopy (MRS), with larger sample sizes may be beneficial.