Background: Magnetic resonance imaging (MRI) in clinical patients is often evaluated for diagnostic purposes. However, to develop a disease classifier, imaging data can be “noisy”, as in being heterogeneous (e.g., obtained from multiple sites), having significant crossover between normal and pathological processes, being highly imbalanced for the outcome variable (i.e., unequal numbers of cases and controls), or due to a lack of accurate quantitative analysis tools that are transferable, easily usable, and accurate to generate the final image variables for machine learning analyses. Methods: In this article, we demonstrate the effectiveness of ComBat harmonization of heterogeneous MRI data on dogs’ brains, collected across multiple sites, prior to using them in the random forest (RF) classifier to attempt to differentiate the meningioma and the glioma tumor-types. We consider three image variables generated from each of the brain scans and three clinical covariates – age, sex, and breedtype – for each subject. The scans are generated either at Colorado State University (CSU) or outside CSU. We compare the RF classifier performance in identifying the two tumor types, with and without preprocessing the data with ComBat site-specific harmonization. Results: The post-ComBat disease classification accuracy measures – sensitivity, specificity, and total accuracy – indicate an overall significant edge in the RF performance compared to their without-ComBat counterparts across different scenarios. Moreover, incorporating both the image variables and the clinical covariates in the RF model results in the highest total accuracy. Conclusions: Use of MRI data in combination with clinical covariates is more informative than using only clinical covariates in classifying meningioma and glioma brain-tumors in dogs. Moreover, as a preprocessing step for MRI data, we recommend adjusting for the site-specific variability using ComBat harmonization prior to performing downstream analyses, such as disease classification.
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