BACKGROUND: Adipose tissue is a valuable biomarker. Although validation and correlation to clinical data have mostly been performed on non-enhanced scans (NES), a previous study has shown conversion of values of contrast enhanced scan (CES) into those of NES to be feasible with segmentation of the entire abdomen (3D-segmentation). In this study we analyzed if density and area of abdominal adipose tissue segmented in a single slice (2D-segmentation) of CES may be converted into that of NES. Furthermore, we compared the precision of conversion between 2D- and 3D-segmentation. METHODS: Thirty-one multi-phasic abdominal CT examinations at identical scan settings were retrospectively included. Exams included NES (n=31), arterial (ART) (n=23), portal-venous (PVN) (n=10), and/or venous scan (VEN) (n=31). Density and area of visceral (VAT) and subcutaneous adipose tissue (SAT) were quantified semi-automatically with fixed thresholds. For conversion of values from CES into those of NES regression analyses were performed and tested. 2D- and 3D-segmentation were compared with respect to conversion accuracy (normalized deviations of converted NES values from original measurements). RESULTS: After the application of contrast medium 2D-segmented adipose tissue increased in density (max. +5.6±2.4 HU) and decreased in area (max. -10.91%) (10.47%), with few exceptions (P<0.05). This was more pronounced in later scans (VEN ≈ PVN > ART) and more marked in VAT than SAT. Density and area in CES correlated very well with NES, allowing for conversion with only small error. While converted density is slightly more precise applying 3D-segmentation, conversion error of quantity was occasionally smaller with 2D-segmentation. CONCLUSIONS: Contrast medium changes density and quantity of segmented adipose tissue in differing degrees between compartments, contrast phases and 2D- and 3D-segmentation. However, changes are fairly constant for a given compartment, contrast phase and mode of segmentation. Therefore, conversion of values into those of NES may be achieved with comparable precision for 2D- and 3D-segmentation. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: Adipose tissue is a valuable biomarker. Although validation and correlation to clinical data have mostly been performed on non-enhanced scans (NES), a previous study has shown conversion of values of contrast enhanced scan (CES) into those of NES to be feasible with segmentation of the entire abdomen (3D-segmentation). In this study we analyzed if density and area of abdominal adipose tissue segmented in a single slice (2D-segmentation) of CES may be converted into that of NES. Furthermore, we compared the precision of conversion between 2D- and 3D-segmentation. METHODS: Thirty-one multi-phasic abdominal CT examinations at identical scan settings were retrospectively included. Exams included NES (n=31), arterial (ART) (n=23), portal-venous (PVN) (n=10), and/or venous scan (VEN) (n=31). Density and area of visceral (VAT) and subcutaneous adipose tissue (SAT) were quantified semi-automatically with fixed thresholds. For conversion of values from CES into those of NES regression analyses were performed and tested. 2D- and 3D-segmentation were compared with respect to conversion accuracy (normalized deviations of converted NES values from original measurements). RESULTS: After the application of contrast medium 2D-segmented adipose tissue increased in density (max. +5.6±2.4 HU) and decreased in area (max. -10.91%) (10.47%), with few exceptions (P<0.05). This was more pronounced in later scans (VEN ≈ PVN > ART) and more marked in VAT than SAT. Density and area in CES correlated very well with NES, allowing for conversion with only small error. While converted density is slightly more precise applying 3D-segmentation, conversion error of quantity was occasionally smaller with 2D-segmentation. CONCLUSIONS: Contrast medium changes density and quantity of segmented adipose tissue in differing degrees between compartments, contrast phases and 2D- and 3D-segmentation. However, changes are fairly constant for a given compartment, contrast phase and mode of segmentation. Therefore, conversion of values into those of NES may be achieved with comparable precision for 2D- and 3D-segmentation. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Melanie Reijrink; Stefanie A de Boer; Daan S Spoor; Joop D Lefrandt; Hiddo J Lambers Heerspink; Ronald Boellaard; Marcel Jw Greuter; Ronald J H Borra; Jan-Luuk Hillebrands; Riemer H J A Slart; Douwe J Mulder Journal: Atherosclerosis Date: 2019-09-25 Impact factor: 5.162
Authors: P Maurovich-Horvat; J Massaro; C S Fox; F Moselewski; C J O'Donnell; U Hoffmann Journal: Int J Obes (Lond) Date: 2006-09-05 Impact factor: 5.095
Authors: Perry J Pickhardt; Peter M Graffy; Ryan Zea; Scott J Lee; Jiamin Liu; Veit Sandfort; Ronald M Summers Journal: Lancet Digit Health Date: 2020-03-02
Authors: Suzanne L Doyle; Anne Marie Bennett; Claire L Donohoe; Ann Marie Mongan; Julia M Howard; Fiona E Lithander; Graham P Pidgeon; John V Reynolds; Joanne Lysaght Journal: Nutr Res Date: 2013-01-30 Impact factor: 3.315
Authors: Borek Foldyna; Fabian M Troschel; Daniel Addison; Florian J Fintelmann; Sammy Elmariah; Deborah Furman; Parastou Eslami; Brian Ghoshhajra; Michael T Lu; Venkatesh L Murthy; Udo Hoffmann; Ravi Shah Journal: J Cardiovasc Comput Tomogr Date: 2018-03-22
Authors: Federico Greco; Rodrigo Salgado; Wim Van Hecke; Romualdo Del Buono; Paul M Parizel; Carlo Augusto Mallio Journal: Quant Imaging Med Surg Date: 2022-03