THU0387 High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

Background There are no validated systems to quantify dermal architecture of skin biopsy sections in systemic sclerosis (SSc). Significant advances in computer vision, called deep neural networks (DNNs), have demonstrated human-level pattern recognition abilities using mathematical transformations of images into millions of quantitative features.1 Publicly available DNN algorithms have the potential to radically augment current histological analyses via robust, reproducible, and high-throughput image quantification. Objectives Apply publicly available DNN algorithms to trichrome-stained sections of dermal biopsies from patients with SSc to identify quantitative features of images that correlate with clinical skin fibrosis assessment via the validated modified Rodnan skin score (mRSS) and skin gene expression.2 Methods One rheumatologist performed local mRSS (lmRSS) assessments (0–3) and two dermal punch biopsies for 7 patients with SSc and one healthy control at baseline and 6, 12, 24, and 36 months. One biopsy was paraffin embedded, sectioned, stained with trichrome, and photomicrographed. Images were transformed into high-level quantitative features using AlexNet and the Matlab Neural Network Toolbox.3 Correlations between quantitative features and lmRSS were determined with Bonferroni-Holm correction. One biopsy underwent gene expression profiling by DNA microarray. The degree of correlation between each gene and lmRSS was assessed and a functional gene network was determined using the GIANT database.4 Results We identified 90 quantitative features that correlated significantly with local skin score (p<0.05, Bonferroni-Holm correction). Using these features, biopsies sorted into three clusters corresponding to low (mean=0.13), intermediate (mean=0.91), and high (mean=1.7) lmRSS. Gene expression for 488 genes in the biopsies correlated significantly with quantitative features (p<0.05, Bonferroni-Holm correction). Among these 488 genes, 185 genes formed a large functional network in the GIANT database including genes associated with the cell cycle, apoptosis, IL12 signalling, and wound healing such as CD44, THBS1, CAV1, and VEGFA. Conclusions This proof-of-principal study demonstrates that DNN processing of stained dermal biopsy sections are sensitive to clinically relevant features of SSc skin. These results suggest that DNNs dramatically expand the quantifiable SSc phenome and that histological samples can now be incorporated into models of SSc. Moreover, our results indicate that the gene expression underlying SSc may be driving histological differences in SSc skin. References [1] CireşAn D, et al. Multi-column deep neural network for traffic sign classification. Neural Networks2012;32:333–338. [2] Rodnan GP, et al. Skin thickness and collagen content in progressive systemic sclerosis and localized scleroderma. Arthritis & Rheumatology1979;22:130–140. [3] Krizhevsky A, et al. ImageNet Classification with Deep Convolutional Neural Networks. Proc. Advances in Neural Information Processing Systems2012;25:1097–1105. [4] Greene CS, et al. Understanding multicellular function and disease with human tissue-specific networks. Nature Genetics2015;47(6):569–576. Acknowledgements NIH NIAMS P30 AR057216; NIAMS K23 AR059763l, L30 AR054311 and Scleroderma Research Foundation. Chase Correia is supported in part by Grant Number T32 AR007611–13 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. Disclosure of Interest None declared