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