# Can multiparametric FDG-PET/MRI analysis really enhance the prediction of myocardial recovery after CTO revascularization? A machine learning study

## TL;DR

This study found that combining FDG-PET and MRI data with machine learning does not improve predictions of heart muscle recovery after CTO revascularization.

## Contribution

The novelty lies in evaluating whether multiparametric FDG-PET/MRI analysis with machine learning improves myocardial recovery prediction in CTO patients.

## Key findings

- Multiparametric FDG-PET/MRI analysis did not improve prediction of wall motion recovery compared to simpler models.
- Machine learning algorithms showed no significant improvement over traditional logistic regression in predicting recovery.
- The benefits of complex FDG-PET/MRI analysis for CTO revascularization remain unproven in this clinical cohort.

## Abstract

•Complex procedures such as CTO revascularization demand reliable prediction of myocardial recovery in the decision-making.•Machine learning-based multiparametric FDG-PET/MRI analysis did not improve the prediction of wall motion recovery after PCI revascularization in a clinical CTO cohort.•The benefits of multiparametric FDG-PET/MRI analysis in this field remain debatable, regardless of the employment of machine learning algorithms.

Complex procedures such as CTO revascularization demand reliable prediction of myocardial recovery in the decision-making.

Machine learning-based multiparametric FDG-PET/MRI analysis did not improve the prediction of wall motion recovery after PCI revascularization in a clinical CTO cohort.

The benefits of multiparametric FDG-PET/MRI analysis in this field remain debatable, regardless of the employment of machine learning algorithms.

To comprehensively evaluate the effectiveness of FDG-PET/MRI multiparametric analysis in predicting myocardial wall motion recovery following revascularization of chronic coronary total occlusions (CTO), incorporating both traditional and machine learning approaches.

This retrospective study assessed fluorine-18 fluorodeoxyglucose uptake (FDG), late gadolinium enhanced MR imaging (LGE), and MR wall motion abnormalities (WMA) of the left ventricle walls of a clinical cohort with 21 CTO patients (62 ± 9 years, 20 men). All patients were examined using a PET/MRI prior to revascularization and a follow-up cardiac MRI six months later. Prediction models for wall motion recovery after perfusion restoration were developed using linear and nonlinear algorithms as well as multiparametric variables. Performance and prediction explainability were evaluated in a 5x2 cross-validation framework, using ROC AUC and McNemar’s test modified for clustered matched-pair data, and Shapley values.

Based on 79 CTO-subtended myocardial wall segments with wall motion abnormalities at baseline, the reference logistic regression model LGE + FDG obtained 0.55(SE = 0.07) in the clustered ROC AUC (cROC AUC) and 0.17(0.05) in the Global Absolute Shapley value. The reference outperformed FDG standalone in cROC AUC (-35(17) %, p < 0.0001), but not LGE standalone (11(12) %, p > 0.05). There were no statistically significant differences between the marginal probabilities of success of these three models. Moreover, no significant improvements (differences < 10 % in cROC AUC, and < 20 % in Global Absolute Shapley, p > 0.05) were found when using mixed effects logistic regression, decision tree, k-nearest neighbor, Naive Bayes, random forest, and support vector machine, with multiparametric combinations of FDG, LGE, and/or WMA.

In this clinical cohort, adding more complex interactions between PET/MRI imaging of cardiac function, infarct extension, and/or metabolism did not enhance the prediction of wall motion recovery after perfusion restoration. This finding raises the question whether multiparametric FDG-PET/MRI analysis has demonstrable benefits in risk stratification for CTO revascularization. Further studies with larger cohorts and external validation datasets are crucial to clarify this question and refine the role of multiparametric imaging in this context.