Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer.
Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer.
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DOI：
10.1016/j.ejrad.2022.110413
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发表时间：
2022-09
影响因子：
3.3
通讯作者：
Gupta, Rajan T.
中科院分区：
文献类型：
--
作者：
Rigiroli, Francesca;Zhang, Dylan;Molinger, Jeroen;Wang, Yingqi;Chang, Andrew;Wischmeyer, Paul E.;Inman, Brant A.;Gupta, Rajan T.
在计算机断层扫描（CT）上手动测量身体成分是耗时的，限制了其临床应用。我们验证了一个软件程序，自动身体成分分析仪使用计算机断层扫描图像分割（ABACS），通过比较其性能手动分割膀胱癌患者队列的身体成分的自动测量。我们对1996年至2017年在杜克大学卫生系统接受膀胱癌治疗的285例患者进行了回顾性分析。使用Slice-O-Matic在L3手动分割腹部CT图像。使用ABACS对相同的L3水平图像进行自动分割。感兴趣的测量是骨骼肌（SM）面积、皮下脂肪组织（SAT）面积和内脏脂肪组织（VAT）面积。SM指数、SAT指数和VAT指数的计算方法是将组成面积除以患者身高2（m2）。使用已发表的临界值，将患者二分为肌肉减少、皮下脂肪过多或内脏脂肪过多。使用Pearson积差相关系数（PPMCC）、类间相关系数（ICC 3）和kappa统计量（κ）评估手动和自动分割之间的一致性。手动和自动分割之间有很强的一致性，对于SM、SAT和VAT区域，PPMCC> 0.90，ICC 3> 0.90。将患者分类为肌肉减少症（κ = 0.73）、皮下脂肪过多（κ = 0.88）或内脏脂肪过多（κ = 0.90）显示出两种方法之间的高度一致性。使用ABACS在CT上对身体成分测量进行自动分割与手动分析类似，并且可以加快身体成分研究中的数据收集。
Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer. We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height2 (m2). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ). There was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods. Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.
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影响因子：
--
作者：
Rossi, Federica;Valdora, Francesca;Tagliafico, Alberto Stefano
通讯作者：
Tagliafico, Alberto Stefano
人体组成分析的软件程序对肥胖成年人的腹部计算机断层扫描扫描。
影响因子：
1.7
作者：
Barbalho, Erica Roberta;Gomes da Rocha, Ilanna Marques;Cunha de Medeiros, Galtieri Otavio;Friedman, Rogerio;Trussardi Fayh, Ana Paula
通讯作者：
Trussardi Fayh, Ana Paula
影响因子：
4.7
作者：
Ozola-Zalite, Manta;Mark, Esben Bolvig;Frokjaer, Jens Brondum
通讯作者：
Frokjaer, Jens Brondum
影响因子：
7
作者：
Prado, C. M.;Cushen, S. J.;Ryan, A. M.
通讯作者：
Ryan, A. M.
影响因子：
8.2
作者：
Wang, Yingqi;Chang, Andrew;Inman, Brant A.
通讯作者：
Inman, Brant A.