Deep learning for quantitative MRI brain tumor analysis

Abstract

The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties, and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. The aim of this study is to investigate if qMRI data provides additional information compared to cMRI sequences (T1w, T1wGd, T2w, FLAIR), when considering deep learning-based brain tumor (1) detection and (2) segmentation. A total of 23 patients with histologically confirmed malignant glioma were retrospectively included in the study. Quantitative MR imaging was used to obtain R1(1/T1), R2(1/T2) and proton density maps pre- and post-gadolinium contrast injection. Conventional MR imaging was also performed. A 2D CNN detection model and a 2D U-Net were trained on transversal slices (n=528) using either cMRI or a combination of qMRI pre- and post-contrast data for tumor detection and segmentation, respectively. Moreover, trends in quantitative R1and R2rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection MCC=0.72, segmentation Dice=0.90), however, improvements were not statistically significant compared to cMRI (detection MCC=0.67, segmentation Dice=0.90). The analysis of the relaxation rates of the relevant regions identified using model explainability methods showed no differences between models trained on cMRI or qMRI. Relevant regions which fell outside the annotation showed changes in relaxation rates after contrast injection similar to those within the annotation, when looking at majority of the individual cases. A similar trend could not be seen when looking at relaxation trends over all the dataset. In conclusion, models trained on qMRI data obtain similar performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within the scan time (11.8 minutes for qMRI with and without contrast, and 12.2 minutes for cMRI). Moreover, when considering individual patients, regions identified by model explainability methods as relevant for tumor detection outside the manual annotation of the tumor showed changes in quantitative relaxation rates after contrast injection similar to regions within the annotation, suggestive of infiltrative tumor in the peritumoral edema.

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

“…A simple and shallow detection model with four convolutional blocks (2D-SDM4) is a custom-made network 32 consisting of 16 layers, of which eight were 2D convolutional layers with a 3x3 kernel and an increasing number of filters from 64 to 512. A detailed description of the 2D-SDM4 model architecture is available in ?…”