High-throughput quantitative histology in systemic sclerosis skin disease using computer vision
| Autor: | Monique Hinchcliff, Rana Saber, Kathleen Aren, Roberta Goncalves Marangoni, Purvesh Khatri, Shane Lofgren, Aileen Hoffmann, J. Matthew Mahoney, Isaac Goldberg, Shannon Teaw, Jungwha Lee, Michelle Cheng, Seamus Mawe, Chase Correia, Shawn E. Cowper |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine lcsh:Diseases of the musculoskeletal system Biopsy Deep neural network Logistic regression Severity of Illness Index Outcome measures AlexNet Scleroderma Cohort Studies Correlation Scleroderma Localized 0302 clinical medicine Fibrosis Computer vision skin and connective tissue diseases Skin Principal Component Analysis Quantitative image features medicine.diagnostic_test integumentary system Middle Aged 3. Good health Cohort Eosine Yellowish-(YS) Systemic sclerosis Female Algorithms Research Article Adult medicine.medical_specialty Histology Outcomes 03 medical and health sciences Deep Learning Methyl Green Internal medicine Linear regression medicine Humans 030203 arthritis & rheumatology Scleroderma Systemic business.industry Modified Rodnan skin score medicine.disease Rheumatology Clinical trial 030104 developmental biology Neural Networks Computer Artificial intelligence lcsh:RC925-935 business Azo Compounds |
| Zdroj: | Arthritis Research & Therapy, Vol 22, Iss 1, Pp 1-11 (2020) Arthritis Research & Therapy |
| ISSN: | 1478-6362 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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