Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{peng:2022:EuroRadiology,
-
author = "Wan-Lin Peng and Tian-Jing Zhang and Ke Shi and
Hai-Xia Li and Ying Li and Sen He and Chen Li and
Dong Xia and Chun-Chao Xia and Zhen-Lin Li",
-
title = "Automatic machine learning based on native T1 mapping
can identify myocardial fibrosis in patients with
hypertrophic cardiomyopathy",
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journal = "European radiology",
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year = "2022",
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volume = "32",
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number = "2",
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pages = "1044--1053",
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month = feb,
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keywords = "genetic algorithms, genetic programming, TPOT,
Cardiomyopathy, Hypertrophic/complications/diagnostic
imaging, Contrast Media, Fibrosis, Gadolinium, Humans,
Machine Learning, Magnetic Resonance Imaging, Cine,
Myocardium/pathology, hypertrophy",
-
ISSN = "1432-1084",
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DOI = "doi:10.1007/s00330-021-08228-7",
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abstract = "OBJECTIVES: To investigate the feasibility of
automatic machine learning (autoML) based on native T1
mapping to predict late gadolinium enhancement (LGE)
status in hypertrophic cardiomyopathy (HCM). METHODS:
Ninety-one HCM patients and 44 healthy controls who
underwent cardiovascular MRI were enrolled. The native
T1 maps of HCM patients were classified as LGE ( +)
or LGE (-) based on location-matched LGE images. An
autoML pipeline was implemented using the tree-based
pipeline optimization tool (TPOT) for 3 binary
classifications: LGE ( +) and LGE (-), LGE (-) and
control, and HCM and control. TPOT modeling was
repeated 10 times to obtain the optimal model for each
classification. The diagnostic performance of the best
models by slice and by case was evaluated using
sensitivity, specificity, accuracy, and microaveraged
area under the curve (AUC). RESULTS: Ten prediction
models were generated by TPOT for each of the 3 binary
classifications. The diagnostic accuracy obtained with
the best pipeline in detecting LGE status in the
testing cohort of HCM patients was 0.80 by slice and
0.79 by case. In addition, the TPOT model also showed
discriminability between LGE (-) patients and control
(accuracy: 0.77 by slice; 0.78 by case) and for all HCM
patients and controls (accuracy: 0.88 for both).
CONCLUSIONS: Native T1 map analysis based on autoML
correlates with LGE ( +) or (-) status. The TPOT
machine learning algorithm could be a promising method
for predicting myocardial fibrosis, as reflected by the
presence of LGE in HCM patients without the need for
late contrast-enhanced MRI sequences. KEY POINTS: The
tree-based pipeline optimization tool (TPOT) is a
machine learning algorithm that could help predict late
gadolinium enhancement (LGE) status in patients with
hypertrophic cardiomyopathy. The TPOT could serve as an
adjuvant method to detect LGE by using information from
native T1 maps, thus avoiding the need for contrast
agent. The TPOT also detects native T1 map alterations
in LGE-negative patients with hypertrophic
cardiomyopathy.",
-
notes = "PMID: 34477909",
- }
Genetic Programming entries for
Wan-Lin Peng
Tian-Jing Zhang
Ke Shi
Hai-Xia Li
Ying Li
Sen He
Chen Li
Dong Xia
Chun-Chao Xia
Zhen-Lin Li
Citations