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Liver shape analysis using statistical parametric maps at population scale.
Thanaj, Marjola; Basty, Nicolas; Cule, Madeleine; Sorokin, Elena P; Whitcher, Brandon; Bell, Jimmy D; Thomas, E Louise.
Afiliação
  • Thanaj M; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK. m.thanaj@westminster.ac.uk.
  • Basty N; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
  • Cule M; Calico Life Sciences LLC, South San Francisco, CA, USA.
  • Sorokin EP; Calico Life Sciences LLC, South San Francisco, CA, USA.
  • Whitcher B; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
  • Bell JD; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
  • Thomas EL; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
BMC Med Imaging ; 24(1): 15, 2024 01 09.
Article em En | MEDLINE | ID: mdl-38195400
ABSTRACT

BACKGROUND:

Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.

METHODS:

Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes.

RESULTS:

We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease.

CONCLUSIONS:

The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 / Hepatopatias Limite: Humans Idioma: En Revista: BMC Med Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 2 / Hepatopatias Limite: Humans Idioma: En Revista: BMC Med Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido