Second-harmonic generation imaging analysis can help distinguish sarcoidosis from tuberculoid leprosy.

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.

Histomorphometric approach to differentiate skin lesions of tuberculoid leprosy from sarcoidosis.

BACKGROUND: More than 200 000 new cases of leprosy are detected worldwide annually. Physicians commonly have difficulty in differentiating tuberculoid form of leprosy (TL) from sarcoidosis' cutaneous manifestation. METHODS: Skin biopsies of 33 patients with TL and 24 with sarcoidosis were reviewed on hematoxylin and eosin- and Gomori-stained sections, in order to find reliable criteria for distinguishing one disease from another. RESULTS: Nine of the 24 features analyzed presented significant predictive value for diagnosis (P < .05). Predominance of tuberculoid granulomas in adnexal and neural distribution, and granulomas replacing the nerves localized within sweat gland glomeruli were predictive to TL diagnosis. For sarcoidosis, dermal fibrosis, back-to-back distribution of the granulomas, presence of atypical giant cells and plasma cells, greater number of conventional giant cells, and spared nerves beside the granuloma were predictive criteria. The median surface density of reticulin fibers was significantly higher in sarcoidosis (3.44) than in TL (2.99). Nonetheless, using logistic regression, this variable did not discriminate between the diseases (P = .096). CONCLUSIONS: Isolated histological features are not fully predictive to differentiate the 2 diseases. However, those with statistical value can assist this distinction in diagnostic practice. Although the results of the analysis of the reticulin fibers density did not tell apart TL from sarcoidosis, they corroborate the idea of fiber fragmentation within tuberculoid leprosy granulomas, reiterating the importance of morphometry in the histological examination.