ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2023
Hybridizing machine learning in survival analysis of cardiac PET/CT imaging.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hybrid PET/CT and PET/MR in Coronary Artery Disease: An Update for Clinicians, with Insights into AI-Guided Integration.Journal of cardiovascular development and disease · 2025Review
- Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.EJNMMI research · 2024Review
- Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies.Current cardiology reports · 2022Review
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Authors and funding
10 authors.
Funding
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Abstract
backgroundMachine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up.
methodsData from 739 intermediate risk patients who underwent coronary CT and selectively stress
resultsCompared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score.
conclusionPrognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation.
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