Department of Traumatology, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands; Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht 6229 HX, the Netherlands.
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
Injury. 2022 Nov;53 Suppl 3:S30-S41. doi: 10.1016/j.injury.2022.05.004. Epub 2022 Jun 2.
Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice.
A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale.
Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating.
A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.
肌少症是一种肌肉疾病，涉及肌肉力量和身体功能的丧失，并与不良健康后果相关。尽管肌少症在文献中受到越来越多的关注，但许多研究结果尚未转化为临床实践。在本文中，我们旨在验证一种用于自动分割 L3 CT 切片的深度学习神经网络，并探索将此类工具用于临床实践的潜在临床应用。
使用来自 3413 例腹部癌症手术患者的多中心数据集对深度学习神经网络进行训练，以自动分割 L3 腰椎水平的肌肉、皮下和内脏脂肪组织。使用 536 例多发伤患者作为独立测试集，以展示其通用性。通过计算 Dice 相似系数来验证几何相似性。使用 Bland-Altman 的界限协议间隔和 Lin 的一致性相关系数来量化定量一致性。为了确定潜在的临床可用性，将随机选择的分割图像呈现给一组经验丰富的临床医生进行李克特量表评分。
深度学习结果与人类专家操作者在所有身体成分指标上均具有极好的一致性，骨骼肌指数的一致性相关系数为 0.92，骨骼肌衰减值为 0.94，内脏脂肪组织指数为 0.99，皮下脂肪组织指数为 0.99。临床医生对分割的三重盲视觉评估仅与 Dice 系数相关，而与无论临床医生的视觉评分如何都准确的定量身体成分指标无关。
一种用于自动分割个体 L3 CT 切片上躯干肌肉、内脏和皮下脂肪组织的深度学习方法已针对扩大的多发伤登记数据集的专家人工生成结果进行了独立验证。与人类专家相比，该方法具有时间效率高、一致性和高精度等优势，提示深度学习的定量身体成分分析可能是一种有前途的临床应用工具，适用于医院环境。
