Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.
Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.
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DOI：
10.2214/ajr.21.26486
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发表时间：
2022-01
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肌肉减少症与不良临床结局相关。用于肌肉减少症评估的基于CT的骨骼肌测量最常在L3椎体水平进行。本文的目的是比较L1与L3水平的全自动深度学习基于CT的肌肉定量在预测未来髋部骨折和死亡方面的效用。这项回顾性研究纳入了9223名无症状成人（平均年龄57 ± 8 [SD]岁; 4071名男性，5152名女性），他们接受了未经增强的低剂量腹部CT。使用先前验证的全自动深度学习工具评估L1和L3水平的肌肉脂肪变性（通过平均衰减）和肌减少（通过横截面积）。比较了L1和L3测量值预测髋部骨折和死亡的性能。还分别使用骨折风险评估工具（FRAX）和Frachial风险评分（FRS）中确定的临床风险评分评价了预测髋部骨折和死亡的性能。CT后的中位临床随访时间为8.8年（四分位距，5.1-11.6年），分别有219例（2.4%）和549例（6.0%）患者发生髋关节骨折和死亡。髋部骨折（p = 0.18 - 0.98）或死亡（p = 0.19 - 0.95）的2年、5年或10年AUC中，L1水平和L3水平肌肉衰减测量值无差异。对于髋部骨折，L1水平肌肉衰减、L3水平肌肉衰减和FRAX评分的5年AUC分别为0.717、0.709和0.708。对于死亡，L1水平肌肉衰减、L3水平肌肉衰减和FRS的5年AUC分别为0.737、0.721和0.688。髋部骨折的最低四分位风险比（HR）分别为2.20（L1衰减）、2.45（L3衰减）和2.53（FRAX评分），死亡的最低四分位风险比（HR）分别为3.25（L1衰减）、3.58（L3衰减）和2.82（FRS）。基于CT的L1和L3肌肉横截面积测量值对髋部骨折和死亡的预测性较低（5年AUC ≤ 0.571; HR ≤ 1.56）。在预测髋部骨折和死亡方面，基于CT的自动测量L1水平的肌肉衰减与先前建立的L3水平测量和临床风险评分相比是有利的。肌减少评估对两个节段结局的预测性较低。替代使用L1而不是L3水平进行基于CT的肌肉测量，允许使用胸部和腹部CT扫描进行肌肉减少症评估，大大增加了机会性CT筛查的潜在收益。
Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1–11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18–.98) or death (p = .19–.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.