Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification

Abstract

Pathologists use multiple microscopy modalities to assess renal biopsy specimens. 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. The proposed system used 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. The final model combined the Gini feature importance set and linear discriminant analysis classifier. Six morphologic (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness greater than ten, glomerular tuft thickness greater than three, 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 77% and 87% were obtained for glomerular and patient-level classification, respectively. 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.

Keywords: basic science; computational pathology; explainable biomarkers; glomerular and tubulointerstitial diseases; machine learning; membranous nephropathy; minimal change disease; thin-basement membrane nephropathy.

Figure 1.

Overview of experimental design. MCD, minimal change disease; MN, membranous nephropathy; ROI, region of interest; TBMN, thin basement membrane nephropathy; TGH, Toronto General Hospital; WSI, whole-slide image.

Figure 2.

Automated glomerular segmentation performance compared to k-means ground truths. (A) Dice similarity coefficient (DSC) measures overlap between segmented object and ground truth, (B) extra fraction (EF) measures false positive rate, (C) precision measures the proportion of correctly segmented pixels relative to the ground truth, and (D) recall measures the proportion of the ground truth pixels that were correctly identified by the predicted segmentation.

Figure 3.

Visual representation of biomarker features extracted. (A) Columns represent sample MCD, MN, and TBMN diseases. (B) Bar and scatterplots visualizing glomerular structure proportions according to structure and disease. (C) Color features: displays the hue histogram for the following three sample images, with their corresponding mean values. (D) Morphologic features: glomerular tuft intrastructural distance feature maps for each corresponding sample image. Thicker structures are represented as red or orange color, whereas thinner structures are green and blue in color. (E) Microstructural texture features: texture maps for sample glomeruli using color vector local binary patterns. Scale bars, 100 µm. MCD, minimal change disease; MN, membranous nephropathy; TBMN, thin basement membrane nephropathy.

Figure 4.

Patient-level confidence results per testing patient. Each patient was predicted with a certain confidence corresponding to TBMN, MN, and MCD. The four highest glomerular probabilities were averaged and then used to get a patient-level confidence. The disease with the largest confidence determined the patient’s predicted diagnosis. Symbols the confidence bars indicates whether the patient was predicted correctly (checkmark) or incorrectly (×). TBMN, thin basement membrane nephropathy; MN, membranous nephropathy; MCD, minimal change disease.

Figure 5.

Correctly classified and misclassified patient WSI, and glomeruli with the highest probabilities. (A) Correctly predicted patient 16 to have MN with 99.65% confidence. (A1–A4) Top four glomeruli ROIs with highest probabilities from (A). (B) Patient 43 was misclassified as having TBMN while truly being diagnosed with MCD, with 62% confidence. (B1–B4) Top four glomeruli ROIs with highest probabilities from (B). WSI, whole-slide image; TBMN, thin basement membrane nephropathy; MN, membranous nephropathy; MCD, minimal change disease.

Figure 6.

Glomerular biomarker distributions. Top four microstructural texture features and top six morphologic features and their respective disease group distributions corresponding to TBMN, MN, and MCD. CV-LBP, color vector local binary patterns; GLCM, gray-level co-occurrence matrices; GT, glomerular tuftTBMN, thin basement membrane nephropathy; MN, membranous nephropathy; MCD, minimal change disease.