Department of Radiology, SMG - SNU Boramae Medical Center, Seoul, Korea.
Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Cancer Imaging. 2023 Aug 2;23(1):73. doi: 10.1186/s40644-023-00593-4.
Loss of muscle mass is the most common complication of end-stage liver disease and negatively affects outcomes for liver transplantation (LT) recipients. We aimed to determine the prognostic value of a fully automated three-dimensional (3D) muscle volume estimation using deep learning algorithms on abdominal CT in patients who underwent liver transplantation (LT).
This retrospective study included 107 patients who underwent LT from 2014 to 2015. Serial CT scans, including pre-LT and 1- and 2-year follow-ups were performed. From the CT scans, deep learning-based automated body composition segmentation software was used to calculate muscle volumes in 3D. Sarcopenia was calculated by dividing average skeletal muscle area by height squared. Newly developed-(ND) sarcopenia was defined as the onset of sarcopenia 1 or 2 years after LT in patients without a history of sarcopenia before LT. Patients' clinical characteristics, including post-transplant diabetes mellitus (PTDM) and Model for end-stage liver disease score, were compared according to the presence or absence of sarcopenia after LT. A subgroup analysis was performed in the post-LT sarcopenic group. The Kaplan-Meier method was used for overall survival (OS).
Patients with ND-sarcopenia had poorer OS than those who did not (P = 0.04, hazard ratio [HR], 3.34; 95% confidence interval [CI] 1.05 - 10.7). In the subgroup analysis for post-LT sarcopenia (n = 94), 34 patients (36.2%) had ND-sarcopenia. Patients with ND-sarcopenia had significantly worse OS (P = 0.002, HR 7.12; 95% CI 2.00 - 25.32) and higher PTDM occurrence rates (P = 0.02, HR 4.93; 95% CI 1.18 - 20.54) than those with sarcopenia prior to LT.
ND-sarcopenia determined by muscle volume on abdominal CT can predict poor survival outcomes and the occurrence of PTDM for LT recipients.
肌肉减少症是终末期肝病最常见的并发症，对肝移植 (LT) 受者的预后产生负面影响。我们旨在确定使用深度学习算法在接受肝移植 (LT) 的患者的腹部 CT 上进行全自动三维 (3D) 肌肉体积估计的预后价值。
本回顾性研究纳入了 2014 年至 2015 年期间接受 LT 的 107 名患者。进行了连续 CT 扫描，包括 LT 前、1 年和 2 年的随访。从 CT 扫描中，使用基于深度学习的自动身体成分分割软件计算 3D 中的肌肉体积。通过将平均骨骼肌面积除以身高的平方来计算肌少症。新发生的肌少症 (ND)-肌少症被定义为在 LT 前无肌少症病史的患者在 LT 后 1 或 2 年内出现肌少症。根据 LT 后是否存在肌少症，比较患者的临床特征，包括移植后糖尿病 (PTDM) 和终末期肝病模型评分。在 LT 后肌少症组进行了亚组分析。采用 Kaplan-Meier 法进行总生存 (OS) 分析。
发生 ND-肌少症的患者的 OS 比未发生肌少症的患者差 (P = 0.04，风险比 [HR]，3.34；95%置信区间 [CI]，1.05-10.7)。在 LT 后肌少症的亚组分析 (n = 94) 中，34 名患者 (36.2%) 发生 ND-肌少症。发生 ND-肌少症的患者的 OS 显著更差 (P = 0.002，HR 7.12；95% CI 2.00-25.32)，PTDM 发生率更高 (P = 0.02，HR 4.93；95% CI 1.18-20.54)，LT 前发生肌少症的患者。
腹部 CT 上的肌肉体积确定的 ND-肌少症可以预测 LT 受者的不良生存结果和 PTDM 的发生。
