Association of visceral fat obesity with structural change in abdominal organs: fully automated three-dimensional volumetric computed tomography measurement using deep learning.
The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295...
| Publicado en: | Abdominal Radiology Vol. 50; no. 9; pp. 4395 - 4403 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187189738&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187189738 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2025 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187189738 182972562 10.1007/s00261-025-04834-x 187189738 ppf: 4395 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Association of visceral fat obesity with structural change in abdominal organs: fully automated three-dimensional volumetric computed tomography measurement using deep learning. aug: au: Kiyoyama, Haruka Tanabe, Masahiro Higashi, Mayumi Kamamura, Naohiko Kawano, Yosuke Ihara, Kenichiro Hideura, Keiko Ito, Katsuyoshi affil: https://ror.org/03cxys317 Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, 755-8505, Ube, Yamaguchi, Japan sug: ab: The purpose of this study was to explore the association between structural changes in abdominal organs and visceral fat obesity (VFO) using a fully automated three-dimensional (3D) volumetric computed tomography (CT) measurement method based on deep learning algorithm. A total of 610 patients (295 men and 315 women; mean age, 68.4 years old) were included. Fully automated 3D volumetric CT measurements of the abdominal organs were performed to determine the volume and average CT attenuation values of each organ. All patients were divided into 2 groups based on the measured visceral fat area: the VFO group (≥ 100 cm2) and non-VFO group (< 100 cm2), and the structural changes in abdominal organs were compared between these groups. The volumes of all organs were significantly higher in the VFO group than in the non-VFO group (all of p < 0.001). Conversely, the CT attenuation values of all organs in the VFO group were significantly lower than those in the non-VFO group (all of p < 0.001). Pancreatic CT values (r = − 0.701, p < 0.001) were most strongly associated with the visceral fat, followed by renal CT values (r = − 0.525, p < 0.001) and hepatic CT values (r = − 0.510, p < 0.001). Fully automated 3D volumetric CT measurement using a deep learning algorithm has the potential to detect the structural changes in the abdominal organs, especially the pancreas, such as an increase in the volumes and a decrease in CT attenuation values, probably due to increased ectopic fat accumulation in patients with VFO. This technique may provide valuable imaging support for the early detection and intervention of metabolic-related diseases. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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