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

Chase Correia, Seamus Mawe, Shane Lofgren, Roberta G. Marangoni, Jungwha Lee, Rana Saber, Kathleen Aren, Michelle Cheng, Shannon Teaw, Aileen Hoffmann, Isaac Goldberg, Shawn E. Cowper, Purvesh Khatri, Monique Hinchcliff*, J. Matthew Mahoney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin. Methods: We analyzed biopsies from two independent (primary and secondary) cohorts. One investigator performed mRSS assessments and forearm biopsies, and trichrome-stained biopsy sections were photomicrographed. We used the AlexNet DNN to generate a numerical signature of 4096 quantitative image features (QIFs) for 100 randomly selected dermal image patches/biopsy. In the primary cohort, we used principal components analysis (PCA) to summarize the QIFs into a Biopsy Score for comparison with mRSS. In the secondary cohort, using QIF signatures as the input, we fit a logistic regression model to discriminate between SSc vs. control biopsy, and a linear regression model to estimate mRSS, yielding Diagnostic Scores and Fibrosis Scores, respectively. We determined the correlation between Fibrosis Scores and the published Scleroderma Skin Severity Score (4S) and between Fibrosis Scores and longitudinal changes in mRSS on a per patient basis. Results: In the primary cohort (n = 6, 26 SSc biopsies), Biopsy Scores significantly correlated with mRSS (R = 0.55, p = 0.01). In the secondary cohort (n = 60 SSc and 16 controls, 164 biopsies; divided into 70% training and 30% test sets), the Diagnostic Score was significantly associated with SSc-status (misclassification rate = 1.9% [training], 6.6% [test]), and the Fibrosis Score significantly correlated with mRSS (R = 0.70 [training], 0.55 [test]). The DNN-derived Fibrosis Score significantly correlated with 4S (R = 0.69, p = 3 × 10- 17). Conclusions: DNN analysis of SSc biopsies is an unbiased, quantitative, and reproducible outcome that is associated with validated SSc outcomes.

Original languageEnglish (US)
Article number48
JournalArthritis Research and Therapy
Volume22
Issue number1
DOIs
StatePublished - Mar 14 2020

Keywords

  • AlexNet
  • Computer vision
  • Deep neural network
  • Histology
  • Modified Rodnan skin score
  • Outcome measures
  • Outcomes
  • Quantitative image features
  • Scleroderma
  • Systemic sclerosis

ASJC Scopus subject areas

  • Rheumatology
  • Immunology and Allergy
  • Immunology

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