Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy

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@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",
• journal = "European radiology",
• year = "2022",
• volume = "32",
• number = "2",
• pages = "1044--1053",
• month = feb,
• 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",
• DOI = "doi:10.1007/s00330-021-08228-7",
• 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