Abstract：
BACKGROUND: Low muscle mass and skeletal muscle mass (SMM) loss are associated with adverse patient outcomes, but the time-consuming nature of manual SMM quantification prohibits implementation of this metric in clinical practice. Therefore, we assessed the feasibility of automated SMM quantification compared to manual quantification. We evaluated both diagnostic accuracy for low muscle mass and associations of SMM (change) with survival in colorectal cancer (CRC) patients.
METHODS: Computed tomography (CT) images from CRC patients enrolled in two clinical studies were analyzed. We compared i) manual vs. automated segmentation of preselected slices at the third lumbar [L3] vertebra (\"semi-automated\"), and ii) manual L3-slice-selection + manual segmentation vs. automated L3-slice-selection + automated segmentation (\"fully-automated\"). Automated L3-selection and automated segmentation was performed with Quantib Body Composition v0.2.1. Bland-Altman analyses, within-subject coefficients of variation (WSCVs) and Intraclass Correlation Coefficients (ICCs) were used to evaluate the agreement between manual and automatic segmentation. Diagnostic accuracy for low muscle mass (defined by an established sarcopenia cut-off) was calculated with manual assessment as the \"gold standard\". Using either manual or automated assessment, Cox proportional hazard ratios (HRs) were used to study the association between changes in SMM (>5% decrease yes/no) during first-line metastatic CRC treatment and mortality adjusted for prognostic factors. SMM change was also assessed separately in weight-stable (<5%, i.e. occult SMM loss) patients.
RESULTS: In total, 1580 CT scans were analyzed, while a subset of 307 scans were analyzed in the fully-automated comparison. Included patients (n = 553) had a mean age of 63 ± 9 years and 39% were female. The semi-automated comparison revealed a bias of -2.41 cm2, 95% limits of agreement [-9.02 to 4.20], a WSCV of 2.25%, and an ICC of 0.99 (95% confidence intervals (CI) 0.97 to 1.00). The fully-automated comparison method revealed a bias of -0.08 cm2 [-10.91 to 10.75], a WSCV of 2.85% and an ICC of 0.98 (95% CI 0.98 to 0.99). Sensitivity and specificity for low muscle mass were 0.99 and 0.89 for the semi-automated comparison and 0.96 and 0.90 for the fully-automated comparison. SMM decrease was associated with shorter survival in both manual and automated assessment (n = 78/280, HR 1.36 [95% CI 1.03 to 1.80] and n = 89/280, HR 1.38 [95% CI 1.05 to 1.81]). Occult SMM loss was associated with shorter survival in manual assessment, but not significantly in automated assessment (n = 44/263, HR 1.43 [95% CI 1.01 to 2.03] and n = 51/2639, HR 1.23 [95% CI 0.87 to 1.74]).
CONCLUSIONS: Deep-learning based assessment of SMM at L3 shows reliable performance, enabling the use of CT measures to guide clinical decision making. Implementation in clinical practice helps to identify patients with low muscle mass or (occult) SMM loss who may benefit from lifestyle interventions.
METHODS: Computed tomography (CT) images from CRC patients enrolled in two clinical studies were analyzed. We compared i) manual vs. automated segmentation of preselected slices at the third lumbar [L3] vertebra (\"semi-automated\"), and ii) manual L3-slice-selection + manual segmentation vs. automated L3-slice-selection + automated segmentation (\"fully-automated\"). Automated L3-selection and automated segmentation was performed with Quantib Body Composition v0.2.1. Bland-Altman analyses, within-subject coefficients of variation (WSCVs) and Intraclass Correlation Coefficients (ICCs) were used to evaluate the agreement between manual and automatic segmentation. Diagnostic accuracy for low muscle mass (defined by an established sarcopenia cut-off) was calculated with manual assessment as the \"gold standard\". Using either manual or automated assessment, Cox proportional hazard ratios (HRs) were used to study the association between changes in SMM (>5% decrease yes/no) during first-line metastatic CRC treatment and mortality adjusted for prognostic factors. SMM change was also assessed separately in weight-stable (<5%, i.e. occult SMM loss) patients.
RESULTS: In total, 1580 CT scans were analyzed, while a subset of 307 scans were analyzed in the fully-automated comparison. Included patients (n = 553) had a mean age of 63 ± 9 years and 39% were female. The semi-automated comparison revealed a bias of -2.41 cm2, 95% limits of agreement [-9.02 to 4.20], a WSCV of 2.25%, and an ICC of 0.99 (95% confidence intervals (CI) 0.97 to 1.00). The fully-automated comparison method revealed a bias of -0.08 cm2 [-10.91 to 10.75], a WSCV of 2.85% and an ICC of 0.98 (95% CI 0.98 to 0.99). Sensitivity and specificity for low muscle mass were 0.99 and 0.89 for the semi-automated comparison and 0.96 and 0.90 for the fully-automated comparison. SMM decrease was associated with shorter survival in both manual and automated assessment (n = 78/280, HR 1.36 [95% CI 1.03 to 1.80] and n = 89/280, HR 1.38 [95% CI 1.05 to 1.81]). Occult SMM loss was associated with shorter survival in manual assessment, but not significantly in automated assessment (n = 44/263, HR 1.43 [95% CI 1.01 to 2.03] and n = 51/2639, HR 1.23 [95% CI 0.87 to 1.74]).
CONCLUSIONS: Deep-learning based assessment of SMM at L3 shows reliable performance, enabling the use of CT measures to guide clinical decision making. Implementation in clinical practice helps to identify patients with low muscle mass or (occult) SMM loss who may benefit from lifestyle interventions.
摘要：
背景：低肌肉质量和骨骼肌质量（SMM）损失与患者不良预后相关，但是手动SMM量化的耗时性质禁止在临床实践中实施该指标.因此,与人工定量相比,我们评估了自动SMM定量的可行性.我们评估了低肌肉质量的诊断准确性以及SMM（变化）与结直肠癌（CRC）患者生存率的关联。
方法：分析两项临床研究中纳入的CRC患者的计算机断层扫描（CT）图像。我们比较了i)手动与自动分割第三腰椎[L3]椎骨的预选切片（“半自动”），和ii)手动L3-切片选择+手动分割与自动L3切片选择+自动分割(“全自动”)。使用Quantib身体成分v0.2.1进行自动L3选择和自动分割。Bland-Altman分析,受试者内变异系数(WSCV)和组内相关系数(ICC)用于评估手动和自动分割之间的一致性。通过手动评估作为“金标准”来计算低肌肉质量的诊断准确性（由已确定的肌肉减少症临界值定义）。使用手动或自动评估，Cox比例风险比（HRs）用于研究一线转移性CRC治疗期间SMM变化（>5％下降是/否）与根据预后因素调整的死亡率之间的关联。SMM变化也在体重稳定的情况下单独评估(<5%，即隐匿性SMM损失)患者。
结果：总计，分析了1580例CT扫描，而在全自动比较中分析了307次扫描的子集.纳入患者（n=553）的平均年龄为63±9岁，39％为女性。半自动比较显示偏差为-2.41cm2，95％的一致性极限[-9.02至4.20]，2.25%的WSCV，ICC为0.99（95%置信区间（CI）0.97至1.00）。全自动比较方法显示偏差为-0.08cm2[-10.91至10.75]，WSCV为2.85%，ICC为0.98（95%CI为0.98至0.99）。半自动比较对低肌肉质量的敏感性和特异性分别为0.99和0.89，全自动比较为0.96和0.90。在手动和自动评估中，SMM降低与较短的生存期相关（n=78/280，HR1.36[95％CI1.03至1.80]和n=89/280，HR1.38[95％CI1.05至1.81]）。在人工评估中，隐匿性SMM丢失与较短的生存期相关，但在自动评估中不显著(n=44/263,HR1.43[95%CI1.01至2.03]和n=51/2639,HR1.23[95%CI0.87至1.74])。
结论：基于深度学习的L3SMM评估显示出可靠的性能，能够使用CT措施来指导临床决策。在临床实践中实施有助于识别可能从生活方式干预中受益的低肌肉质量或（隐匿性）SMM损失的患者。
方法：分析两项临床研究中纳入的CRC患者的计算机断层扫描（CT）图像。我们比较了i)手动与自动分割第三腰椎[L3]椎骨的预选切片（“半自动”），和ii)手动L3-切片选择+手动分割与自动L3切片选择+自动分割(“全自动”)。使用Quantib身体成分v0.2.1进行自动L3选择和自动分割。Bland-Altman分析,受试者内变异系数(WSCV)和组内相关系数(ICC)用于评估手动和自动分割之间的一致性。通过手动评估作为“金标准”来计算低肌肉质量的诊断准确性（由已确定的肌肉减少症临界值定义）。使用手动或自动评估，Cox比例风险比（HRs）用于研究一线转移性CRC治疗期间SMM变化（>5％下降是/否）与根据预后因素调整的死亡率之间的关联。SMM变化也在体重稳定的情况下单独评估(<5%，即隐匿性SMM损失)患者。
结果：总计，分析了1580例CT扫描，而在全自动比较中分析了307次扫描的子集.纳入患者（n=553）的平均年龄为63±9岁，39％为女性。半自动比较显示偏差为-2.41cm2，95％的一致性极限[-9.02至4.20]，2.25%的WSCV，ICC为0.99（95%置信区间（CI）0.97至1.00）。全自动比较方法显示偏差为-0.08cm2[-10.91至10.75]，WSCV为2.85%，ICC为0.98（95%CI为0.98至0.99）。半自动比较对低肌肉质量的敏感性和特异性分别为0.99和0.89，全自动比较为0.96和0.90。在手动和自动评估中，SMM降低与较短的生存期相关（n=78/280，HR1.36[95％CI1.03至1.80]和n=89/280，HR1.38[95％CI1.05至1.81]）。在人工评估中，隐匿性SMM丢失与较短的生存期相关，但在自动评估中不显著(n=44/263,HR1.43[95%CI1.01至2.03]和n=51/2639,HR1.23[95%CI0.87至1.74])。
结论：基于深度学习的L3SMM评估显示出可靠的性能，能够使用CT措施来指导临床决策。在临床实践中实施有助于识别可能从生活方式干预中受益的低肌肉质量或（隐匿性）SMM损失的患者。
