Handling confounding variables in statistical shape analysis -- application to cardiac remodelling

TL;DR

This paper introduces a linear statistical shape analysis framework with confounding correction methods to accurately identify shape differences related to cardiac remodelling, especially in imbalanced datasets with confounders.

Contribution

It presents new confounding deflation and adjustment techniques integrated into shape analysis to account for demographic variables affecting shape variability.

Findings

Confirmed increased ventricular volumes and myocardial mass in athletes.

Demonstrated the necessity of confounder correction in imbalanced datasets.

Validated robustness of the methods through subset analyses.

Abstract

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.