Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

Abstract

Background Pathologists use multiple microscopy modalities to assess renal biopsies. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof of principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light microscopy level. Methods The proposed system employed image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. Results The final model combined the Gini feature importance set and Linear Discriminant Analysis classifier. Six morphological (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness > 10, glomerular tuft thickness > 3, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were together the best performing biomarkers. Accuracies of 76.86% and 86.67% were obtained for glomerular and patient-level classification, respectively. Conclusion Computational methods using explainable glomerular biomarkers have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.