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Background: Labial salivary gland biopsy (LSGB) is the most important diagnostic tool for the diagnosis of Sjögren's syndrome (SS), but its diagnostic value is rarely studied. This study assessed the sensibility and specificity of LSGB, and the clinical profiles of patients who were referred for biopsy. Methods: Retrospective analysis of the histopathological reports from LSGB and medical report data from patients who underwent LSGB between 2008 and 2011 was conducted. Results: About 290 biopsies were performed and 74 were excluded due to insufficient clinical data. Of the 216 patients, 0.46% was carrier of hepatitis C virus, 30.1% had primary SS (pSS), and 8.8% had secondary SS (sSS). Of the samples, 94.3% presented dryness symptoms, 51.6% experienced dryness only, 42.7% had systemic manifestations, and 66.9% presented low unstimulated salivary flow and/or Schirmer's test. LSGB was necessary in 67.6% to confirm the presence of SS based on the American-European Consensus Group 2002 criteria (AECG). Based on specialist's opinion, sensibility level was 86.57%, and specificity was 97.43%. Positive predictive value (PPV) was 95%, and negative predictive value (NPV) was 92.6%. Determined accuracy was 93.3%. Concordance (kappa coefficient) of LSGB and specialist's opinion was 0.851, and LSGB with AECG criteria was 0.806. Of the 98 patients referred with fibromyalgia and dryness, 36.7% had SS and LSBG focus score of = 1. Patients with SS were older, and showed more severe lachrymal and salivary dysfunctions, greater frequency of fibromyalgia, anti-nuclear antibodies (ANA), anti-SSA-Ro, and anti-SSB-La. Conclusions: Labial salivary gland biopsy has high sensibility, specificity, positive and negative predictive values for diagnosis of pSS. In the clinical practice, it is useful, especially for those patients with glandular dysfunctions and negative antibodies. [ABSTRACT FROM AUTHOR]

In recent years, interest in applying deep learning (DL) to medical diagnosis has rapidly increased, driven primarily by the development of Convolutional Neural Networks and Transformers. Despite advancements in DL, the automated diagnosis of skin cancer remains a significant challenge. Emulating dermatologists, deep learning approaches using clinical images acquired from smartphones and considering patient lesion information have achieved performance levels close to those of specialists. While including clinical information, such as whether the lesion bleeds, hurts, or itches, improves diagnostic metrics, it is insufficient for correctly differentiating some major skin cancer lesions. An alternate technology for diagnosing skin cancer is fluorescence widefield imaging, where the skin lesion is illuminated with excitation light, causing it to emit fluorescence. Since there is no public dataset of fluorescence images for skin lesions, to the best of our knowledge, an effort has been made and resulted in 1,563 fluorescence images of major skin lesions taken by smartphones using the handheld LED wieldfield fluorescence device. The collected images were annotated and analyzed, creating a new dataset named FLUO-SC. Convolutional neural networks were then applied to classify skin lesions using these fluorescence images. Experimental results indicate that fluorescence images are competitive with clinical images (baseline) for classifying major skin lesions and show promising potential for discrimination., (Copyright © 2024. Published by Elsevier B.V.)

Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)