#6891 MACHINE LEARNING NEPHROPATHOLOGY DIAGNOSTICS OF THROMBOTIC MICROANGIOPATHIES

Abstract

Abstract Background and Aims Thrombotic microangiopathies, comprising atypical hemolytic uremic syndrome (aHUS) and other diseases, can present with a broad clinical and histopathological spectrum. On our way to an evidence-base for the nephropathological work-up of TMAs, we have chosen a machine-learning approach, thus eliminating suboptimal reproducibility of descriptors for individual lesions in the three decisive compartments artery, arteriole and glomerulus with human experts. Here, we present our results for an end-to-end diagnostic system. Method We collected 50 random biopsies with TMAs of various etiologies (including aHUS, hypertension-associated, systemic sclerosis, anti-phospholipid antibody syndrome and others) and 50 biopsies with Mimickers (differential diagnoses of TMA), including severe hypertensive nephropathy, necrotising arteritis/arteriolitis, cryoglobulinemic vasculitis from the three participating centers Cologne, Weill-Cornell Medical Center, and Turin. Whole slide images (WSIs) from all four nephropathology stainings HE, PAS, trichrome and Jones were included in this study. We developed an instance segmentation Mask-RCNN model with a Swin Transformer (t) backbone on tissue crops detected using a lightweight variant of the U-Net segmentation architecture. For the classification model we used our own MorphSet++ set transformer architecture to process batches of EfficientNetv2s-encoded tissue crops entered in three separate compartment channels. Batches were chosen with Monte Carlo sampling or using our own soft Markov Chain Monte Carlo (MCMC) approach. Results of the classification model are reported with 5-fold internal cross-validation. Results Segmentation performance measured as mIOU, mAP, mAR, mF1, mAS for artery reached 0.565, 0.739, 0.679, 0.704, 0.995, for arteriole 0.342, 0.531, 0.488, 0.490, 0.996, for glomeruli 0.818, 0.880, 0.919, 0.896, 0.993. Classification accuracy reached 90% with no false positives for TMA. Missed cases of TMA could be salvaged by an experienced nephropathologist on the display of decisive compartment crops, which were selected using model confidence averaged across each sampling iteration. Conclusion We have designed and trained architectures capable of segmenting decisive compartments and diagnosing TMAs on renal biopsy sections. This will enable automatic analysis of clinicopathological datasets with TMA in large cohorts. Our ultimate goal is to use large cohorts from collaborating institutions for weakly supervised, case-level-annotated training of diagnostic, prognostic and theranostic classifiers.