Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis

Thejal Srikumar, Erin M. Siegel, Yuhua Gu, Yoganand Balagurunathan, Alberto L. Garcia, Y. Ann Chen, Jun Min Zhou, Xiuhua Zhao, Robert Gillies, Whalen Clark, Andrew Gamenthaler, Junsung Choi, David Shibata

Research output: Contribution to journalArticle

Abstract

Background: The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis. Materials and methods: We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m 2 ± 6.56 kg/m 2 . The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level. Results: The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio. Conclusions: This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

Original languageEnglish (US)
Pages (from-to)12-21
Number of pages10
JournalJournal of Surgical Research
Volume237
DOIs
StatePublished - May 1 2019

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Subcutaneous Fat
Adiposity
Intra-Abdominal Fat
Tomography
Radiology Information Systems
Fats
Adipose Tissue
Adenocarcinoma
Body Mass Index
Obesity

All Science Journal Classification (ASJC) codes

  • Surgery

Cite this

Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis. / Srikumar, Thejal; Siegel, Erin M.; Gu, Yuhua; Balagurunathan, Yoganand; Garcia, Alberto L.; Chen, Y. Ann; Zhou, Jun Min; Zhao, Xiuhua; Gillies, Robert; Clark, Whalen; Gamenthaler, Andrew; Choi, Junsung; Shibata, David.

In: Journal of Surgical Research, Vol. 237, 01.05.2019, p. 12-21.

Research output: Contribution to journalArticle

Srikumar, T, Siegel, EM, Gu, Y, Balagurunathan, Y, Garcia, AL, Chen, YA, Zhou, JM, Zhao, X, Gillies, R, Clark, W, Gamenthaler, A, Choi, J & Shibata, D 2019, 'Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis', Journal of Surgical Research, vol. 237, pp. 12-21. https://doi.org/10.1016/j.jss.2018.11.027
Srikumar, Thejal ; Siegel, Erin M. ; Gu, Yuhua ; Balagurunathan, Yoganand ; Garcia, Alberto L. ; Chen, Y. Ann ; Zhou, Jun Min ; Zhao, Xiuhua ; Gillies, Robert ; Clark, Whalen ; Gamenthaler, Andrew ; Choi, Junsung ; Shibata, David. / Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis. In: Journal of Surgical Research. 2019 ; Vol. 237. pp. 12-21.
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AU - Siegel, Erin M.

AU - Gu, Yuhua

AU - Balagurunathan, Yoganand

AU - Garcia, Alberto L.

AU - Chen, Y. Ann

AU - Zhou, Jun Min

AU - Zhao, Xiuhua

AU - Gillies, Robert

AU - Clark, Whalen

AU - Gamenthaler, Andrew

AU - Choi, Junsung

AU - Shibata, David

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N2 - Background: The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis. Materials and methods: We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m 2 ± 6.56 kg/m 2 . The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level. Results: The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio. Conclusions: This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

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