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

Title: Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma
Authors: Mi, E
Mauricaite, R
Pakzad-Shahabi, L
Chen, J
Ho, A
Williams, M
Item Type: Journal Article
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.
Issue Date: 30-Nov-2021
Date of Acceptance: 6-Oct-2021
URI: http://hdl.handle.net/10044/1/95871
DOI: 10.1038/s41416-021-01590-9
ISSN: 0007-0920
Publisher: Springer Nature [academic journals on nature.com]
Start Page: 196
End Page: 203
Journal / Book Title: British Journal of Cancer
Volume: 126
Issue: 2
Copyright Statement: © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Science & Technology
Life Sciences & Biomedicine
Oncology
NEWLY-DIAGNOSED GLIOBLASTOMA
HYPOFRACTIONATED RADIOTHERAPY
CANCER
SEGMENTATION
SARCOPENIA
MASS
SURVIVAL
STANDARD
THERAPY
Adult
Aged
Deep Learning
Female
Glioblastoma
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
Muscle, Skeletal
Prognosis
Sarcopenia
Survival Rate
Young Adult
Muscle, Skeletal
Humans
Glioblastoma
Magnetic Resonance Imaging
Prognosis
Survival Rate
Image Processing, Computer-Assisted
Adult
Aged
Middle Aged
Female
Male
Young Adult
Sarcopenia
Deep Learning
Science & Technology
Life Sciences & Biomedicine
Oncology
NEWLY-DIAGNOSED GLIOBLASTOMA
HYPOFRACTIONATED RADIOTHERAPY
CANCER
SEGMENTATION
SARCOPENIA
MASS
SURVIVAL
STANDARD
THERAPY
Oncology & Carcinogenesis
1112 Oncology and Carcinogenesis
1117 Public Health and Health Services
Publication Status: Published
Online Publication Date: 2021-11-30
Appears in Collections:Department of Surgery and Cancer



This item is licensed under a Creative Commons License Creative Commons