A1 Refereed original research article in a scientific journal

Hybridizing machine learning in survival analysis of cardiac PET/CT imaging




AuthorsJuarez-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

PublisherSPRINGER

Publication year2023

JournalJournal of Nuclear Cardiology

Journal name in sourceJOURNAL OF NUCLEAR CARDIOLOGY

Journal acronymJ NUCL CARDIOL

Number of pages10

ISSN1071-3581

eISSN1532-6551

DOIhttps://doi.org/10.1007/s12350-023-03359-4

Web address https://doi.org/10.1007/s12350-023-03359-4

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/181181489


Abstract

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.


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Last updated on 2024-26-11 at 22:54