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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Evaluation and Comparative Correlation of Abdominal Fat Related Parameters in Obese and Non-obese Groups Using Computed Tomography

Author(s): Kompalli J. Satya Siva Raghu Teja, Bhamidipaty Kanaka Durgaprasad* and Payala Vijayalakshmi

Volume 17, Issue 3, 2021

Published on: 08 October, 2020

Page: [417 - 424] Pages: 8

DOI: 10.2174/1573405616666201008145801

Abstract

Background: Obesity is a significant risk factor for cardiovascular (CV) disease. Abdominal fat is composed of abdominal subcutaneous fat and intra-abdominal (visceral) fat. Computed tomography (CT) is considered one of the most accurate and reliable methods for assessing abdominal fat.

Introduction: The present study was based on evaluating abdominal fat by computed tomography and the determination of association between CT obtained abdominal fat volumes, anthropometric indices, and lipid profile.

Methods: The prospective study was carried out on 120 subjects referred to the Radiology department for a CT scan. Non - contrast CT scan was performed with 5 mm slice thickness. Abdominal fat volumes were recorded by using CT attenuation values (- 250 to -50 HU). The section was selected at the level of the umbilicus (L4-L5). Intra-abdominal fat and subcutaneous fat volumes were calculated. Body Mass Index (BMI) and lipid profile were recorded for each subject. A comparative study of the CT values, BMI, and lipid profile was undertaken.

Results: In the present study, by comparing the anthropometric parameters, CT findings, and lipid profile and blood parameters of the obese and non-obese groups by sex revealed significant sex differences in all the parameters under study. It was also found that the obese male and female groups showed a high prevalence of diabetes, Non-Alcoholic fatty liver disease (NAFLD), and hypertension than non-obese groups. This finding also adds to the chances of getting cardiovascular diseases, specifically in obese individuals. The results found that in obese males and females the abdominal fat-related parameters Visceral fatty acid (VFA) and subcutaneous fatty acid (SFA) showed highly significant relation to anthropometric parameters like BMI, waist circumference (WC) and waist/hip (W/H) ratio on the other hand blood parameters high-density lipoprotein (HDL), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), total cholesterol and triglycerides to some extent have a significant relation to abdominal fat-related parameters. In non-obese groups, by studying the influence of anthropometric parameters on abdominal fat-related parameters, it was revealed that WC was strongly affected by the VFA in both sexes. In obese females, more fat was accumulated in the VFA and SFA and for obese males in SFA and for non-obese males in total fatty acid (TFA).

Conclusion: Computed tomography assessed visceral fat area remains the most sensitive independent predictor of cardiovascular risk.

Keywords: Body fat distribution, cardiovascular diseases/diagnosis, computed tomography, visceral fat, abdominal fat, obesity.

Graphical Abstract

[1]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[2]
Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys 2014; 41(5): 050902.
[http://dx.doi.org/10.1118/1.4871620] [PMID: 24784366]
[3]
Meyer P, Noblet V, Mazzara C, Lallement A. Survey on deep learning for radiotherapy. Comput Biol Med 2018; 98: 126-46.
[http://dx.doi.org/10.1016/j.compbiomed.2018.05.018] [PMID: 29787940]
[4]
Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys 2017; 44(2): 547-57.
[http://dx.doi.org/10.1002/mp.12045] [PMID: 28205307]
[5]
Lustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol 2018; 126(2): 312-7.
[http://dx.doi.org/10.1016/j.radonc.2017.11.012] [PMID: 29208513]
[6]
Dalmış MU, Litjens G, Holland K, et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys 2017; 44(2): 533-46.
[http://dx.doi.org/10.1002/mp.12079] [PMID: 28035663]
[7]
Qin W, Wu J, Han F, et al. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Phys Med Biol 2018; 63(9): 095017.
[http://dx.doi.org/10.1088/1361-6560/aabd19] [PMID: 29633960]
[8]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with Deep Neural Networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[9]
Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys 2017; 44(12): 6377-89.
[http://dx.doi.org/10.1002/mp.12602] [PMID: 28963779]
[10]
Sudre CH, Li WQ, Vercauteren T, Ourselin S, Cardoso MJ. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Lect Notes Comput Sci 2017; 10553: 240-8.
[http://dx.doi.org/10.1007/978-3-319-67558-9_28]
[11]
Ren X, Xiang L, Nie D, et al. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images. Med Phys 2018; 45(5): 2063-75.
[http://dx.doi.org/10.1002/mp.12837] [PMID: 29480928]
[12]
Bartalena L, Marcocci C, Pinchera A. Orbital radiotherapy for Graves’ ophthalmopathy. J Clin Endocrinol Metab 2004; 89(1): 13-4.
[http://dx.doi.org/10.1210/jc.2003-031769] [PMID: 14715819]
[13]
Mourits MP, van Kempen-Harteveld ML, García MBG, Koppeschaar HPF, Tick L, Terwee CB. Radiotherapy for Graves’ orbitopathy: randomised placebo-controlled study. Lancet 2000; 355(9214): 1505-9.
[http://dx.doi.org/10.1016/S0140-6736(00)02165-6] [PMID: 10801172]
[14]
Donaldson SS, Bagshaw MA, Kriss JP. Supervoltage orbital radiotherapy for Graves’ ophthalmopathy. J Clin Endocrinol Metab 1973; 37(2): 276-85.
[http://dx.doi.org/10.1210/jcem-37-2-276] [PMID: 4198257]
[15]
Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation. Proc Cvpr Ieee 2015; pp. 3431-40.
[16]
Jia YQ, Shelhamer E, Donahue J, et al. Embedding Proceedings of the 2014 Acm Conference on Multimedia (Mm'14). 675-8.
[17]
Ravishankar H, Sudhakar P, Venkataramani R, et al. Understanding the mechanisms of deep transfer learning for medical images. Deep Learn Data Labeling Med Appl 2016; 10008: 188-96.
[http://dx.doi.org/10.1007/978-3-319-46976-8_20]
[18]
Zou KH, Warfield SK, Bharatha A, et al. Statistical validation of image segmentation quality based on a spatial overlap index Acad Radiol 2004; 11(2): 178-89.
[http://dx.doi.org/10.1016/S1076-6332(03)00671-8]
[19]
Popovic A, de la Fuente M, Engelhardt M, Radermacher K. Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Ass Rad 2007; 2(3-4): 169-81.
[http://dx.doi.org/10.1007/s11548-007-0125-1]
[20]
Alt H, Scharf L. Computing the Hausdorff distance between curved objects. Int J Comput Geom Appl 2008; 18(4): 307-20.
[http://dx.doi.org/10.1142/S0218195908002647]
[21]
Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G. SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 2012; 39(10): 6332-8.
[http://dx.doi.org/10.1118/1.4754659] [PMID: 23039669]
[22]
Tsuji SY, Hwang A, Weinberg V, Yom SS, Quivey JM, Xia P. Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010; 77(3): 707-14.
[http://dx.doi.org/10.1016/j.ijrobp.2009.06.012] [PMID: 20231063]
[23]
Walker GV, Awan M, Tao R, et al. Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer. Radiother Oncol 2014; 112(3): 321-5.
[http://dx.doi.org/10.1016/j.radonc.2014.08.028] [PMID: 25216572]
[24]
Kosmin M, Ledsam J, Romera-Paredes B, et al. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother Oncol 2019; 135: 130-40.
[http://dx.doi.org/10.1016/j.radonc.2019.03.004] [PMID: 31015159]
[25]
Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. J Med Radiat Sci 2014; 61(3): 131-4.
[http://dx.doi.org/10.1002/jmrs.65] [PMID: 26229648]
[26]
Zeng L, Xie XQ, Li CH, Shi HS, Wang F. Clinical study of the radiotherapy with EDGE accelerator in the treatment of the moderate and severe thyroid associated ophthalmopathy. Eur Rev Med Pharmacol Sci 2019; 23(8): 3471-7.
[PMID: 31081102]

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