A1 Refereed original research article in a scientific journal
Hybridizing machine learning in survival analysis of cardiac PET/CT imaging
Authors: Juarez-Orozco Luis Eduardo, Niemi Mikael, Yeung Ming Wai, Benjamins Jan Walter, Maaniitty Teemu, Teuho Jarmo, Saraste Antti, Knuuti Juhani, van der Harst Pim, Klén Riku
Publisher: SPRINGER
Publication year: 2023
Journal: Journal of Nuclear Cardiology
Journal name in source: JOURNAL OF NUCLEAR CARDIOLOGY
Journal acronym: J NUCL CARDIOL
Number of pages: 10
ISSN: 1071-3581
eISSN: 1532-6551
DOI: https://doi.org/10.1007/s12350-023-03359-4
Web address : https://doi.org/10.1007/s12350-023-03359-4
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/181181489
Background
Machine 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.
Methods
Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model.
Results
Compared 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.
Conclusion
Prognostic 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.
Downloadable publication This is an electronic reprint of the original article. |