Miller William, Fate Kassandra, Fisher Jessica, Thul Jessica, Ko Yousun, Kim Kyung Won, Pruett Timothy, Teigen Levi
Division of Solid Organ Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
University of Minnesota Medical School, Minneapolis, Minnesota, USA.
Clin Transplant. 2025 Jun;39(6):e70201. doi: 10.1111/ctr.70201.
Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography (CT) scans are utilized to measure patient core musculature as a measurement of sarcopenia. Methods to extract information on core body musculature can be through either freehand region-of-interest (ROI) or machine learning algorithms to quantitate total body muscle within a given area. This study directly compares these two collection methods leveraging length of stay (LOS) outcomes previously found to be associated with freehand ROI measurements.
A total of 50 individuals were included who underwent liver transplantation from our single center between January 1, 2016, and May 30, 2021, and had a non-contrast abdominal CT scan within 6-months of surgery. CT-derived skeletal muscle measures at the third lumbar vertebrae were obtained using freehand ROI and an automated deep learning system.
Correlation analysis of freehand psoas muscle measures, psoas area index (PAI) and mean Hounsfield units (mHU), were significantly correlated to the automated deep learning system's total skeletal muscle measures at the level of the L3, skeletal muscle index (SMI) and skeletal muscle density (SMD), respectively (R = 0.4221; p value < 0.0001; R = 0.6297; p value < 0.0001). The automated deep learning model's SMI predicted ∼20% of the variability (R = 0.2013; hospital length of stay) while the PAI variable only predicted about 10% of the variability (R = 0.0919; total healthcare length of stay) of the length of stay variables. In contrast, both the freehand ROI mHU and the automated deep learning model's muscle density variables were associated with ∼20% of the variability in the inpatient length of stay (R = 0.2383 and 0.1810, respectively) and total healthcare length of stay variables (R = 0.2190 and 0.1947, respectively).
Sarcopenia measurements represent an important risk stratification tool for liver transplantation outcomes. For muscle sarcopenia assessment association with LOS, freehand measures of sarcopenia perform similarly to automated deep learning system measurements.
肌肉减少症，即肌肉质量和数量的丧失，与肝移植的不良临床结局相关，如感染、住院时间延长和患者死亡率增加。腹部计算机断层扫描（CT）用于测量患者的核心肌肉组织，作为肌肉减少症的一项指标。提取核心身体肌肉组织信息的方法可以是通过徒手绘制感兴趣区域（ROI），也可以是通过机器学习算法来量化给定区域内的全身肌肉。本研究直接比较了这两种收集方法，利用先前发现与徒手ROI测量相关的住院时间（LOS）结果。
纳入了2016年1月1日至2021年5月30日期间在我们单中心接受肝移植且在术后6个月内进行了非增强腹部CT扫描的50名个体。使用徒手ROI和自动深度学习系统获得第三腰椎水平的CT衍生骨骼肌测量值。
徒手测量的腰大肌测量值、腰大肌面积指数（PAI）和平均亨氏单位（mHU）的相关性分析，分别与自动深度学习系统在L3水平的全身骨骼肌测量值、骨骼肌指数（SMI）和骨骼肌密度（SMD）显著相关（R = 0.4221；p值<0.0001；R = 0.6297；p值<0.0001）。自动深度学习模型的SMI预测了约20%的变异性（R = 0.2013；住院时间），而PAI变量仅预测了住院时间变量约10%的变异性（R = 0.0919；总医疗住院时间）。相比之下，徒手ROI的mHU和自动深度学习模型的肌肉密度变量均与住院时间变异性的约20%相关（分别为R = 0.2383和0.1810）以及总医疗住院时间变量（分别为R = 0.2190和0.19(7)相关。
肌肉减少症测量是肝移植结局的重要风险分层工具。对于与住院时间相关的肌肉减少症评估，徒手肌肉减少症测量与自动深度学习系统测量表现相似。
