Cargando…

Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our go...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Xiaoya, Maehara, Akiko, Yang, Mingming, Wang, Liang, Zheng, Jie, Samady, Habib, Mintz, Gary S., Giddens, Don P., Tang, Dalin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9127388/
https://www.ncbi.nlm.nih.gov/pubmed/35620594
http://dx.doi.org/10.3389/fphys.2022.912447
_version_ 1784712342221619200
author Guo, Xiaoya
Maehara, Akiko
Yang, Mingming
Wang, Liang
Zheng, Jie
Samady, Habib
Mintz, Gary S.
Giddens, Don P.
Tang, Dalin
author_facet Guo, Xiaoya
Maehara, Akiko
Yang, Mingming
Wang, Liang
Zheng, Jie
Samady, Habib
Mintz, Gary S.
Giddens, Don P.
Tang, Dalin
author_sort Guo, Xiaoya
collection PubMed
description Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models. Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up. Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively. Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression.
format Online
Article
Text
id pubmed-9127388
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91273882022-05-25 Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach Guo, Xiaoya Maehara, Akiko Yang, Mingming Wang, Liang Zheng, Jie Samady, Habib Mintz, Gary S. Giddens, Don P. Tang, Dalin Front Physiol Physiology Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models. Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up. Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively. Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression. Frontiers Media S.A. 2022-05-10 /pmc/articles/PMC9127388/ /pubmed/35620594 http://dx.doi.org/10.3389/fphys.2022.912447 Text en Copyright © 2022 Guo, Maehara, Yang, Wang, Zheng, Samady, Mintz, Giddens and Tang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Guo, Xiaoya
Maehara, Akiko
Yang, Mingming
Wang, Liang
Zheng, Jie
Samady, Habib
Mintz, Gary S.
Giddens, Don P.
Tang, Dalin
Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title_full Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title_fullStr Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title_full_unstemmed Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title_short Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach
title_sort predicting coronary stenosis progression using plaque fatigue from ivus-based thin-slice models: a machine learning random forest approach
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9127388/
https://www.ncbi.nlm.nih.gov/pubmed/35620594
http://dx.doi.org/10.3389/fphys.2022.912447
work_keys_str_mv AT guoxiaoya predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT maeharaakiko predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT yangmingming predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT wangliang predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT zhengjie predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT samadyhabib predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT mintzgarys predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT giddensdonp predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach
AT tangdalin predictingcoronarystenosisprogressionusingplaquefatiguefromivusbasedthinslicemodelsamachinelearningrandomforestapproach