Handling confounding variables in statistical shape analysis -- application to cardiac remodelling
This work addresses the issue of confounding bias in medical imaging analysis for researchers and clinicians, though it is incremental as it builds on existing shape analysis methods.
The authors tackled the problem of confounding variables in statistical shape analysis by developing a linear framework with two correction methods, which they applied to cardiac MRI data from 89 triathletes and 77 controls to identify exercise-induced cardiac remodelling. Their results showed that without correction, no increase in myocardial mass was detected, but with adjustment, real remodelling patterns emerged in imbalanced datasets.
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.