Dermal type I collagen assessment by digital image analysis

Type I collagen is the main dermal component, and its evaluation is relevant to quantitative studies in dermatopathology. However, visual gradation (0 to 4+) has low precision and high subjectivity levels. This study aimed to develop and validate a digital morphometric analysis technique to estimate...

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Bibliographic Details
Published in:Anais brasileiros de dermatología Vol. 90; no. 5; pp. 723 - 727
Main Authors: Brianezi, Gabrielli, Grandi, Fabrizio, Bagatin, Ediléia, Enokihara, Mílvia Maria S. S., Miot, Hélio Amante
Format: Journal Article
Language:English
Published: Brazil Sociedade Brasileira de Dermatologia 01.10.2015
Subjects:
Aged
Algorithms
Biopsy
Collagen
Collagen Type I
Collagen Type I - analysis
DERMATOLOGY
Dermatopathology
Dermis - anatomy & histology
Humans
Image Interpretation, Computer-Assisted
Image Processing, Computer-Assisted
Image Processing, Computer-Assisted - methods
Immunohistochemistry
Middle Aged
Observer Variation
Reproducibility of Results
Staining and Labeling - methods
Statistics, Nonparametric
Immunohistochemistry
Collagen Type I
Image Processing, Computer-Assisted
Collagen
ISSN:0365-0596, 1806-4841, 0365-0596, 1806-4841
Online Access:Get full text
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Summary:Type I collagen is the main dermal component, and its evaluation is relevant to quantitative studies in dermatopathology. However, visual gradation (0 to 4+) has low precision and high subjectivity levels. This study aimed to develop and validate a digital morphometric analysis technique to estimate type I collagen levels in the papillary dermis. Four evaluators visually quantified (0 to 4+) the density of type I collagen in 63 images of forearm skin biopsies marked by immunohistochemistry and two evaluators analyzed the same images using digital morphometric techniques (RGB split colors (I) and color deconvolution (II)). Automated type I collagen density estimation in the papillary dermis (two techniques) were correlated with visual evaluations (Spearman's rho coefficients of 0.48 and 0.62 (p<0.01)). With regard to the inter-observer repeatability, the four evaluators who used visual classification had an intraclass correlation coefficient (for absolute agreement) of 0.53, while the other two evaluators who used digital analysis (algorithm II) had an intraclass correlation coefficient of 0.97.
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ISSN:0365-0596
1806-4841
0365-0596
1806-4841
DOI:10.1590/abd1806-4841.20153331