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Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images

Ananya Singh, Jacek Kwieciński, Robert J.H. Miller, Yuka Otaki, Paul Kavanagh, Serge D. Van Kriekinge, Tejas Parekh, Heidi Gransar, Konrad Pieszko, Aditya Killekar, Ramyashree Tummala, Joanna X. Liang, Marcelo F. Di Carli, Daniel S. Berman, Damini Dey, Piotr J. Slomka

2022Circulation Cardiovascular Imaging41 citationsDOIOpen Access PDF

Abstract

Background: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. Methods: A total of 4735 consecutive patients referred for stress and rest 82 Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24–6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation. Results: In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77–0.86]) was higher than ischemia (0.60 [95% CI, 0.54–0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64–0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69–0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%–46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia. Conclusions: The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.

Topics & Concepts

MedicineQuartilePositron emission tomographyMyocardial perfusion imagingProspective cohort studyCardiologyInternal medicineLogistic regressionPerfusionReceiver operating characteristicPerfusion scanningArea under the curveMyocardial infarctionNuclear medicineConfidence intervalCardiac Imaging and DiagnosticsAutopsy Techniques and OutcomesCardiovascular Function and Risk Factors