Journal article
Right ventricular strain as a key feature in interpretable machine learning for identification of Takotsubo syndrome: a multicenter CMR-based study
Academic radiology, Vol.32(9), pp.5039-5051
09/2025
DOI: 10.1016/j.acra.2025.04.068
PMID: 40404506
Abstract
To develop an interpretable machine learning (ML) model based on cardiac magnetic resonance (CMR) multimodal parameters and clinical data to discriminate Takotsubo syndrome (TTS), acute myocardial infarction (AMI), and acute myocarditis (AM), and to further assess the diagnostic value of right ventricular (RV) strain in TTS.
This study analyzed CMR and clinical data of 130 patients from three centers. Key features were selected using least absolute shrinkage and selection operator regression and random forest. Data were split into a training cohort and an internal testing cohort (ITC) in the ratio 7:3, with overfitting avoided using leave-one-out cross-validation and bootstrap methods. Nine ML models were evaluated using standard performance metrics, with Shapley additive explanations (SHAP) analysis used for model interpretation.
A total of 11 key features were identified. The extreme gradient boosting model showed the best performance, with an area under the curve (AUC) value of 0.94 (95% CI: 0.85–0.97) in the ITC. Right ventricular basal circumferential strain (RVCS-basal) was the most important feature for identifying TTS. Its absolute value was significantly higher in TTS patients than in AMI and AM patients (−9.93%, −5.21%, and −6.18%, respectively, p < 0.001), with values above −6.55% contributing to a diagnosis of TTS.
This study developed an interpretable ternary classification ML model for identifying TTS and used SHAP analysis to elucidate the significant value of RVCS-basal in TTS diagnosis. An online calculator (https://lsszxyy.shinyapps.io/XGboost/) based on this model was developed to provide immediate decision support for clinical use.
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Details
- Title: Subtitle
- Right ventricular strain as a key feature in interpretable machine learning for identification of Takotsubo syndrome: a multicenter CMR-based study
- Creators
- Zeliu Du - Wenzhou Medical UniversityHongfei Hu - Lishui Central HospitalChenqi Sheng - Wenzhou Medical UniversityJie Mei - Lishui Central HospitalYe Feng - Wenzhou Medical UniversityYechao Huang - Lishui Central HospitalXinyu Chen - Wenzhou Medical UniversityXinyu Guo - Lishui Central HospitalZhanning Hu - Wenzhou Medical UniversityLiyan Jiang - Lishui Central HospitalYanping Su - Lishui Central HospitalJumatay Biekan - Circle Cardiovascular ImagingLingchun Lyv - Zhejiang UniversityTouKun Chong - Kiang Wu HospitalCunxue Pan - Fifth Affiliated Hospital of Sun Yat-sen UniversityKan Liu - Washington University in St. LouisJiansong Ji - Wenzhou Medical UniversityChenying Lu - Wenzhou Medical University
- Resource Type
- Journal article
- Publication Details
- Academic radiology, Vol.32(9), pp.5039-5051
- DOI
- 10.1016/j.acra.2025.04.068
- PMID
- 40404506
- NLM abbreviation
- Acad Radiol
- ISSN
- 1076-6332
- eISSN
- 1878-4046
- Publisher
- Elsevier Inc
- Grant note
- National Key Research and Development Program of China: 2024YFC2417600 Medical and Health Research Project of Zhejiang Province: 2025KY498
This study has been supported by the National Key Research and Development Program of China (2024YFC2417600) and the Medical and Health Research Project of Zhejiang Province (2025KY498) .
- Language
- English
- Electronic publication date
- 05/21/2025
- Date published
- 09/2025
- Academic Unit
- Cardiovascular Medicine; Internal Medicine
- Record Identifier
- 9984824301302771
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