Deep learning-based segmentation and quantification of temporalis muscle for sarcopenia assessment is an independent prognostic factor in glioblastoma


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Ella Mi, Radvile Mauricaite, Lillie Pakzad-Shahabi, Katherine Pike, Matthew Williams

Abstract

Background

Glioblastoma multiforme (GBM) is an aggressive brain malignancy that is difficult to prognosticate. Sarcopenia is associated with poor survival in cancer and is assessable on cross-sectional imaging, but manual muscle quantification is time-consuming and susceptible to interrater inconsistency. The aim of this study was to develop a fully-automated deep learning-based system for temporalis muscle quantification, a sarcopenia surrogate on cranial MRI, and determine whether it predicts survival in GBM.

Method

Retrospective cohort study of 152 MR studies in 45 patients with newly-diagnosed GBM from 01/2015 - 05/2018. A neural network for automated temporalis segmentation was trained and validated on three loss functions, and used to quantify muscle cross-sectional area (CSA). The primary outcome was effect of temporalis CSA on overall (OS) and progression-free survival (PFS) in GBM. Secondary outcomes were OS and PFS by CSA in age-/sex-disaggregated subgroups and at different longitudinal timepoints.

Results

Mean (SD) age was 54.3 ± 11.0 and 34/45 (75.6%) patients were males. The model achieved high accuracy in segmenting temporalis with Dice coefficient of 0.912 ± 0.031, Hausdorff distance of 1.813 ± 0.390mm and CSA error of 1.94 ± 8.86%. Dice loss conferred best model performance. CSA was a significant predictor of OS (HR 0.506, 95%CI 0.267-0.959; p=0.037) and PFS (HR 0.348, 95%CI 0.182-0.667; p=0.001). CSA is an independent prognostic factor; in multivariate analysis, HR was 0.456 for OS (95%CI 0.207-1.004; p=0.051) and 0.309 for PFS (95%CI 0.131-0.730; p=0.007). The effect of CSA on OS/PFS is independently significant and particularly strong in younger patients (HR 0.213, 95%CI 0.058-0.788; p=0.020/HR 0.053, 95%CI 0.009-0.302; p=0.001) and males (HR 0.400, 95%CI 0.196-0.986; p=0.046/HR 0.173, 95%CI 0.059-0.509; p=0.001), and persists at the post-treatment timepoint.

Conclusion

Temporalis muscle area is a significant prognostic marker in GBM, and can be automatically, rapidly and accurately assessed using a deep learning-based system.

Impact statement

This is the first study, in any cancer type, to apply deep learning to muscle segmentation for sarcopenia assessment and demonstrate prognostic significance, establishing a role for deep learning in sarcopenia screening in cancer, which adds significant value to routine imaging and has clinical application for risk profiling, treatment stratification and interventions for muscle preservation.