Computational Oncology Group, Institute of Global Health Innovation, Imperial College London, London, UK.
Department of Radiotherapy, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK.
Br J Cancer. 2022 Feb;126(2):196-203. doi: 10.1038/s41416-021-01590-9. Epub 2021 Nov 30.
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.
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.
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).
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.
胶质母细胞瘤是最常见的恶性脑肿瘤。肌肉减少症与癌症存活率下降有关，但手动在影像学上量化肌肉是很耗时的。我们提出了一种基于深度学习的方法，用于量化颞肌（骨骼肌质量的替代指标），并评估其在胶质母细胞瘤中的预后价值。
使用来自 4 个不同胶质母细胞瘤数据集的 132 名患者的 366 个 MRI 头部图像来训练颞肌分割神经网络，并用于量化肌肉横截面积（CSA）。在内部和外部数据集的 96 名胶质母细胞瘤患者中，确定了颞肌 CSA 与生存之间的关联。
该模型达到了很高的分割准确性（Dice 系数为 0.893）。内部和 TCGA-GBM 数据集中患者的中位年龄分别为 55 岁和 58 岁，男性分别占 75.6%和 64.7%。CSA 是内部和 TCGA-GBM 数据集中生存的独立显著预测因子（HR 0.464，95%CI 0.218-0.988，p=0.046；HR 0.466，95%CI 0.235-0.925，p=0.029）。
颞肌 CSA 是胶质母细胞瘤患者的预后标志物，可通过深度学习快速准确地评估。我们是第一个显示使用深度学习生成的头颈部肌肉减少症指标与肿瘤学结果相关的研究之一，也是第一个显示基于深度学习的肌肉定量具有癌症预后价值的研究之一。
