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Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions


Title: Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions
Author: Ricciardi, Carlo   orcid.org/0000-0001-7290-6432
Edmunds, Kyle   orcid.org/0000-0002-6591-4116
Recenti, Marco   orcid.org/0000-0001-9440-8434
Sigurðsson, Sigurður
Gudnason, Vilmundur   orcid.org/0000-0001-5696-0084
Carraro, Ugo
Gargiulo, Paolo   orcid.org/0000-0002-5049-4817
Date: 2020-02-18
Language: English
Scope: 2863
University/Institute: Háskóli Íslands
University of Iceland
Háskólinn í Reykjavík
Reykjavik University
School: Heilbrigðisvísindasvið (HÍ)
School of Health Sciences (UI)
Tæknisvið (HR)
School of Technology (RU)
Department: Læknadeild (HÍ)
Faculty of Medicine (UI)
Verkfræðideild (HR)
Department of Engineering (RU)
Series: Scientific Reports;10(1)
ISSN: 2045-2322
DOI: 10.1038/s41598-020-59873-9
Subject: Cardiovascular risks; CT; NTRA; Kransæðasjúkdómar
URI: https://hdl.handle.net/20.500.11815/2156

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Citation:

Ricciardi, C., Edmunds, K.J., Recenti, M. et al. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Scientific Reports 10, 2863 (2020). https://doi.org/10.1038/s41598-020-59873-9

Abstract:

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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