Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort.

OBJECTIVE To investigate a fully automated CT-based adiposity tool, applying it to a longitudinal adult screening cohort. METHODS A validated automated adipose tissue segmentation algorithm was applied to non-contrast abdominal CT scans in 8852 consecutive asymptomatic adults (mean age, 57.1 years; 3926 M/4926 F) undergoing colonography screening. The tool was also applied to follow-up CT scans in a subset of 1584 individuals undergoing longitudinal surveillance (mean interval, 5.6 years). Visceral and subcutaneous adipose tissue (VAT and SAT) volumes were segmented at levels T12-L5. Primary adipose results are reported herein for the L1 level as mean cross-sectional area. CT-based adipose measurements at initial CT and change over time were analyzed. RESULTS Mean VAT values were significantly higher in males (205.8 ± 107.5 vs 108.1 ± 82.4 cm2; p < 0.001), whereas mean SAT values were significantly higher in females (171.3 ± 111.3 vs 124.3 ± 79.7 cm2; p < 0.001). The VAT/SAT ratio at L1 was three times higher in males (1.8 ± 0.7 vs 0.6 ± 0.4; p < 0.001). At longitudinal follow-up CT, mean VAT/SAT ratio change was positive in males, but negative in females. Among the 502 individuals where the VAT/SAT ratio increased at follow-up CT, 333 (66.3%) were males. Half of patients (49.6%; 786/1585) showed an interval increase in both VAT and SAT at follow-up CT. CONCLUSION This robust, fully automated CT adiposity tool allows for both individualized and population-based assessment of visceral and subcutaneous abdominal fat. Such data could be automatically derived at abdominal CT regardless of the study indication, potentially allowing for opportunistic cardiovascular risk stratification. Advances in knowledge: The CT-based adiposity tool described herein allows for fully automated measurement of visceral and subcutaneous abdominal fat, which can be used for assessing cardiovascular risk, metabolic syndrome, and for change over time.

[1]  P. Pickhardt,et al.  Opportunistic Screening for Osteoporosis Using Body CT Scans Obtained for Other Indications: the UW Experience , 2017, Clinical Reviews in Bone and Mineral Metabolism.

[2]  P. Pickhardt,et al.  Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications , 2013, Annals of Internal Medicine.

[3]  P. Pickhardt.  Imaging and Screening for Colorectal Cancer with CT Colonography. , 2017, Radiologic clinics of North America.

[4]  Young Jae Kim,et al.  Computerized Automated Quantification of Subcutaneous and Visceral Adipose Tissue From Computed Tomography Scans: Development and Validation Study , 2016, JMIR medical informatics.

[5]  B. Cho,et al.  Distribution of Abdominal Visceral and Subcutaneous Adipose Tissue and Metabolic Syndrome in a Korean Population , 2011, Diabetes Care.

[6]  Ronald M. Summers,et al.  Associations among Pericolonic Fat, Visceral Fat, and Colorectal Polyps on CT Colonography , 2014, Obesity.

[7]  Adelino F. Leite-Moreira,et al.  El cociente entre la grasa abdominal visceral y la subcutánea evaluado por tomografía computarizada es un predictor independiente de mortalidad y eventos cardiacos , 2017 .

[8]  T. Yoshimoto,et al.  Ratio of visceral‐to‐subcutaneous fat area predicts cardiovascular events in patients with type 2 diabetes , 2017, Journal of Diabetes Investigation.

[9]  A Tremblay,et al.  Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. , 1994, The American journal of cardiology.

[10]  J. Murabito,et al.  The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk , 2012, Diabetologia.

[11]  Robin Strand,et al.  Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies , 2017, Scientific Reports.

[12]  Philippe Pibarot,et al.  Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. , 2008, Arteriosclerosis, thrombosis, and vascular biology.

[13]  A. Kafatos,et al.  Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index , 2000, International Journal of Obesity.

[14]  Noriyuki Moriyama,et al.  Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT. , 2008, Nihon Hoshasen Gijutsu Gakkai zasshi.

[15]  Perry J Pickhardt,et al.  Predicting Future Hip Fractures on Routine Abdominal CT Using Opportunistic Osteoporosis Screening Measures: A Matched Case-Control Study. , 2017, AJR. American journal of roentgenology.

[16]  Estanislao Arana,et al.  Opportunistic screening for osteoporosis by routine CT in Southern Europe , 2017, Osteoporosis International.

[17]  C. Morrison,et al.  Waist circumference as a measure for indicating need for weight management , 1995, BMJ.

[18]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[19]  Pim A. de Jong,et al.  Opportunistic screening for osteoporosis on routine computed tomography? An external validation study , 2015, European Radiology.

[20]  Udo Hoffmann,et al.  Abdominal Visceral and Subcutaneous Adipose Tissue Compartments: Association With Metabolic Risk Factors in the Framingham Heart Study , 2007, Circulation.

[21]  Jiamin Liu,et al.  Visceral fat quantification in asymptomatic adults using abdominal CT: is it predictive of future cardiac events? , 2014, Abdominal Imaging.

[22]  A. Tjønneland,et al.  Self-reported and technician-measured waist circumferences differ in middle-aged men and women. , 2005, The Journal of nutrition.

[23]  Sören Mattsson,et al.  Development of methods for body composition studies , 2006, Physics in medicine and biology.

[24]  Rickey E Carter,et al.  Validation study of a new semi-automated software program for CT body composition analysis , 2017, Abdominal Radiology.

[25]  Young Jae Kim,et al.  Body Fat Assessment Method Using CT Images with Separation Mask Algorithm , 2013, Journal of Digital Imaging.

[26]  S. Khosla,et al.  Comprehensive Assessment of Osteoporosis and Bone Fragility with CT Colonography. , 2016, Radiology.

[27]  Binsheng Zhao,et al.  Automated Quantification of Body Fat Distribution on Volumetric Computed Tomography , 2006, Journal of computer assisted tomography.

[28]  Ronald M. Summers,et al.  Association between visceral adiposity and colorectal polyps on CT colonography. , 2012, AJR. American journal of roentgenology.

[29]  Cornelis H. Slump,et al.  Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans , 2011, Medical Imaging.

[30]  Laurence E. Court,et al.  Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images , 2017, PloS one.

[31]  R. Ross,et al.  Visceral Fat Is an Independent Predictor of All‐cause Mortality in Men , 2006, Obesity.

[32]  Ronald M. Summers,et al.  Fully automated adipose tissue measurement on abdominal CT , 2011, Medical Imaging.

[33]  Luigi Ferrucci,et al.  Automated abdominal fat quantification and food residue removal in CT , 2012, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis.

[34]  J. K. Brown,et al.  Asynchronously Calibrated Quantitative Bone Densitometry. , 2017, Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry.