Machine learning based analytics of micro-MRI trabecular bone microarchitecture and texture in type 1 Gaucher disease

Abstract

Type 1 Gaucher disease (GD) is an autosomal recessive lysosomal storage disease, affecting bone metabolism, structure and strength. Current bone assessment methods are not ideal. Semi-quantitative MRI scoring is unreliable, not standardized, and only evaluates bone marrow. DXA BMD is also used but is a limited predictor of bone fragility/fracture risk. Our purpose was to measure trabecular bone microarchitecture, as a biomarker of bone disease severity, in type 1 GD individuals with different GD genotypes and to apply machine learning based analytics to discriminate between GD patients and healthy individuals. Micro-MR imaging of the distal radius was performed on 20 type 1 GD patients and 10 healthy controls (HC). Fifteen stereological and textural measures (STM) were calculated from the MR images. General linear models demonstrated significant differences between GD and HC, and GD genotypes. Stereological measures, main contributors to the first two principal components (PCs), explained ~50% of data variation and were significantly different between males and females. Subsequent PCs textural measures were significantly different between GD patients and HC individuals. Textural measures also significantly differed between GD genotypes, and distinguished between GD patients with normal and pathologic DXA scores. PCA and SVM predictive analyses discriminated between GD and HC with maximum accuracy of 73% and area under ROC curve of 0.79. Trabecular STM differences can be quantified between GD patients and HC, and GD sub-types using micro-MRI and machine learning based analytics. Work is underway to expand this approach to evaluate GD disease burden and treatment efficacy.