A fully automated deep learning pipeline to assess muscle mass in brain tumor patients

Background: Brain tumors are the leading cause of cancer death in the under-40s. The commonest malignant brain tumor is a Glioblastoma multiforme (GBM) with less than 5% 5-year survival. Low skeletal muscle mass is associated with poor survival in cancer and is measurable on routine imaging, but manual muscle mass quantification is time-consuming and susceptible to interrater inconsistency. In patients with brain tumors, measurement of the thickness of the temporalis muscle acts as a proxy. We present a fully-automated deep learning-based system for temporalis muscle quantification, a skeletal muscle mass surrogate on MRI head. Methods: MRI scans of 330 patients were obtained from four different datasets. Two 2D U-Nets were trained, one to segment the eyeballs and the other to segment the temporalis muscle, and used to quantify the cross-sectional area of the eyeballs and temporalis muscle. The eyeball segmentation was used to chose a consistent level on which to assess temporalis muscle mass. We assessed accuracy of segmentation using DICE and Hausdorff scores, and assessed the performance of the system to choose the correct slice on the MRI by comparing it with a manual choice of MRI slice. Results: The set models predict eyeball and temporalis muscle segmentations with good accuracy. Mean Dice scores is
$0.90\pm 0.03$
and
$0.90\pm 0.05$
and Hausdorff distances are
$2.88\pm 0.60$
and
$1.89\pm 0.35$
, respectively. The automatic pipeline chooses slices for segmentation that are either identical or close to the manual choice in 96.1% of cases. Conclusions: We have developed an end-to-end system that uses two independently trained U-Nets to first segment the eyeball and then use that as a reference point to pick the correct slice of the MRI on which to use the second U-Net to measure temporalis cross-sectional area. This allows for the automated processing of head MRI scans to measure temporalis muscle mass, which has been previously shown to correlate with body muscle mass and survival in several cancer types.