Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma

Abstract

Background: Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma.

Methods: A neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets.

Results: The model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218-0.988, p = 0.046; HR 0.466, 95% CI 0.235-0.925, p = 0.029, respectively).

Conclusions: Temporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.

Fig. 1. Automated temporalis segmentations.

Three representative test set MRI head images (T1 weighted + GAD contrast) with overlay of predicted temporalis muscle segmentations by the neural network.

Fig. 2. Comparison of ground truth and automated temporalis segmentation muscle areas.

Bland–Altman plot comparing cross-sectional areas of manual and predicted temporalis muscle segmentations in the test set.

Fig. 3. Relationship between temporalis muscle area and age.

Distribution of temporalis muscle area vs age in patients in the a in-house glioblastoma patient data set and b TCGA-GBM data set. CSA cross-sectional area.

Fig. 4. Relationship between temporalis muscle area and survival in glioblastoma.

Kaplan–Meier survival curves for overall survival (a) and progression-free survival (b) by temporalis muscle area group in the in-house glioblastoma patient data set and overall survival by temporalis muscle area group in the TCGA-GBM data set (c). CSA cross-sectional area.