Abstract
Sarcoidosis and tuberculoid leprosy (TL) are prototypes of granulomatous
inflammation in dermatology, which embody one of the histopathology
limitations in distinguishing some diseases. Recent advances in the use
of nonlinear optical microscopy in skin have enabled techniques, such as
second-harmonic generation (SHG), to become powerful tools to study the
physical and biochemical properties of skin. We use SHG images to
analyze the collagen network, to distinguish differences between
sarcoidosis and TL granulomas. SHG images obtained from skin biopsies of
33 patients with TL and 24 with sarcoidosis retrospectively were
analyzed using first-order statistics (FOS) and second-order statistics,
such as gray-level co-occurrence matrix (GLCM). Among the four
parameters evaluated (optical density, entropy, contrast, and second
angular moment), only contrast demonstrated statistical significance, being higher in sarcoidosis (p = 0.02; 4908.31 versus 2822.17). The
results may indicate insufficient differentiating power for most tested
FOS and GLCM parameters in classifying sarcoidosis and TL granulomas,
when used individually. But in combination with histopathology (H&E and
complementary stains, such as silver and fast acid stains), SHG
analysis, like contrast, can contribute to distinguishing between these
diseases. This study can provide a way to evaluate collagen distribution
in granulomatous diseases. (c) The Authors. Published by SPIE under a
Creative Commons Attribution 3.0 Unported License.
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