Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
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DOI：
10.1148/radiol.2020201640
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发表时间：
2021-02-01
期刊：
影响因子：
19.7
通讯作者：
Rosenthal, Michael H.
中科院分区：
文献类型：
--
作者：
Magudia, Kirti;Bridge, Christopher P.;Rosenthal, Michael H.
背景资料：虽然基于CT的身体成分（BC）指标可以告知疾病风险和结果，但获得这些指标对于大规模使用来说过于资源密集。因此，BC在人群中的分布仍不确定。目的：为了证明腹部CT检查中全自动深度学习BC分析的有效性，定义人口统计学调整的BC参考曲线，并说明与标准方法相比使用这些曲线的优势，沿着其在预测生存率方面的生物学意义。材料和方法：经过外部确认和手动分割的等效性测试后，一个完全自动化的深度学习BC分析管道被应用于一个跨部分人群队列，包括任何在三家医院之一接受腹部CT检查的无心血管疾病或癌症的门诊患者，2012.为每个BC区域生成人口统计学调整的人群参考曲线。通过使用卡方检验将从这些曲线得出的z分数与肌肉减少症的性别特异性阈值进行比较，并用于在包括体重和体重指数（BMI）的多变量考克斯比例风险模型中预测2年生存率。外部验证显示出良好的相关性（R = 0.99）和等效性（P < .001）的全自动深度学习BC分析方法与手动分割。使用来自12128名门诊患者（平均年龄52岁; 6936名[57%]女性）的全自动BC数据，生成年龄、种族和性别标准化BC参考曲线。所有BC面积随这些变量显著变化（除皮下脂肪面积与年龄[P =.003]外，P < .001）。肌肉减少症的性别特异性阈值表明，如果使用来自参考曲线的z评分，则不存在年龄和种族偏倚（P < .001）。在包括BMI.Conclusion的组合模型中，骨骼肌面积z评分显著预测2年生存率（P =.04）：全自动身体成分（BC）指标因年龄、种族和性别而异。与标准方法相比，从BC参数的参考曲线导出的z评分更好地捕获BC的人口统计学分布，并且可以帮助预测生存。(C)RSNA，2020年
Background: Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain.Purpose: To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curves, and to illustrate the advantage of use of these curves compared with standard methods, along with their biologic significance in predicting survival.Materials and Methods: After external validation and equivalency testing with manual segmentation, a fully automated deep learning BC analysis pipeline was applied to a cross-sectional population cohort that included any outpatient without a cardiovascular disease or cancer who underwent abdominal CT examination at one of three hospitals in 2012. Demographically adjusted population reference curves were generated for each BC area. The z scores derived from these curves were compared with sex-specific thresholds for sarcopenia by using chi(2) tests and used to predict 2-year survival in multivariable Cox proportional hazards models that included weight and body mass index (BMI).Results: External validation showed excellent correlation (R = 0.99) and equivalency (P < .001) of the fully automated deep learning BC analysis method with manual segmentation. With use of the fully automated BC data from 12 128 outpatients (mean age, 52 years; 6936 [57%] women), age-, race-, and sex-normalized BC reference curves were generated. All BC areas varied significantly with these variables (P < .001 except for subcutaneous fat area vs age [P =.003]). Sex-specific thresholds for sarcopenia demonstrated that age and race bias were not present if z scores derived from the reference curves were used (P < .001). Skeletal muscle area z scores were significantly predictive of 2-year survival (P =.04) in combined models that included BMI.Conclusion: Fully automated body composition (BC) metrics vary significantly by age, race, and sex. The z scores derived from reference curves for BC parameters better capture the demographic distribution of BC compared with standard methods and can help predict survival. (C) RSNA, 2020