Tag Archives: histopathology

Epidemiological and histological characteristics of cutaneous squamous cell carcinoma and its precursor lesions – A single-center study

DOI: 10.2478/amma-2023-0043

Objective: Cutaneous squamous cell carcinoma (cSCC) is a skin malignancy that is one of the non-melanocytic skin cancers (NMSCs). The objective of our study was to highlight the epidemiological and histological characteristics of cSCC diagnosed in a clinical county hospital.
Methods: A retrospective cross-sectional study was performed of histopathologically diagnosed cases of cSCC from the clinical Pathology Department of the Mures Clinical County Hospital, Târgu Mureș, Romania. We included 96 cases that were diagnosed between January 1, 2017, and December 31, 2020.
Results: Of the 96 cases included in the study, 82 were identified as cSCC, 5 as Bowen Disease, and 9 as keratoacanthoma. The majority of the cases were diagnosed in 2018 (n = 30; 31.25%) and 2019 (n=36; 37.50%). The median age of the patients was 63.0 years. Slightly over half of the patients were male (n=50; 52.08) and 49 patients (51.04%) grew up in urban areas. Forty-six cases (56.10%) were well differentiated; 25 (30.49%) moderately differentiated, and 11 (13.41%) poorly differentiated. Almost all of the lesions (93; 96.88%) were removed within the safety excision margins.
Conclusion: Most of the patients were diagnosed with cSCC in 2018 and 2019 and were over 70 years old. The majority were males who grew up in urban areas. Even though most of the lesions were well differentiated and completely excised surgically, the differential diagnoses between cSCC and other skin malignancies were made based on the morphological aspects of the lesions, followed by an immunohistochemical profile when necessary.

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Artificial intelligence based model for establishing the histopathological diagnostic of the cutaneous basal cell carcinoma

DOI: 10.2478/amma-2022-0020

Introduction: Artificial intelligence (AI), a component of computer science, has the ability to process the multitude of medical data existing in the medical system around the world. The goal of our study is to build an AI model, based on Machine Learning, capable of assisting pathologists around the world in the diagnosis of the basal cell carcinoma of the skin.
Material and Method: Our study is represented by the development of a Mask-RCNN (Mask Region-based Convolutional Neural Network) model, for the detection of cells with typical basal cell carcinoma tumoral changes. A number of 258 digitized histological images were used. The images emerged from Hematoxylin&Eosin stained pathology slides, diagnosed with cutaneous basal cell carcinoma between January 2018 and December 2021, at the Pathology Service of the Mureș County Clinical Hospital.
Results: All the used images have the unique resolution of 2560×1920 pixels. For the learning process, we divided these images into two datasets: the learning dataset, representing 80% of the total images; and the test dataset, representing 20% of the total images. The AI ​​model was trained using 1000 epochs with a learning rate of 0.00025 and only one classification category: basal cell carcinoma.
Conclusions: The AI model successfully identified in 85% of the cases the areas with pathological changes present in the input images.

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Single-plaque psoriasis: a single-clue diagnostic challenge

DOI: 10.2478/amma-2022-0019

Introduction: Psoriasis is a chronic, common immune inflammatory condition of the skin, affecting 2-3% of the population, with regional variability. Classically, psoriasis presents as one of the following types: plaque, guttate, inverse, pustular or erythrodermic psoriasis. Typically, the patient will present with several symmetric psoriatic plaques on typical areas of the body, leading the clinician towards the diagnosis of psoriasis.
Case report: The present case report series focuses on an atypical presentation of psoriasis noted in 2 patients who presented to our office with a single large, erythematous plaque located on the lower leg. Due to poor response to previous treatment, a biopsy was performed and upon analysis, revealed a diagnosis of psoriasis. The lesions showed significant improvement under local therapy.
Conclusion: In spite of significant research on such a common and seemingly well-understood dermatosis, the present case reports plead for further study with regards to atypical presentations of psoriasis.

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Mediators of Inflammation as a Link Between Diabetes Mellitus and Periodontal Breakdown

DOI: 10.2478/amma-2018-0005

Our objective was to investigate immunological changes that occur in saliva of subjects with type 2 diabetes mellitus (T2DM) without signs of periodontal disease and to establish if salivary inflammatory cytokines are a possible link between diabetes mellitus and periodontal breakdown. Material and methods. Twenty T2DM subjects with no periodontal disease and twenty healthy controls were registered for the present study. TNF-α and IL-6 level from saliva and serum were measured. Periodontal tissue samples were histologically examined.

Results: TNF-α and IL-6 levels were higher in T2DM subjects compared to controls, with an extremely significant difference in saliva (p<0.001). Significant inflammation, affecting both epithelial and connective tissues was present in periodontal biopsies. Conclusions: The subjects showed an increased TNF-α and IL-6 levels, both in serum and -mostly in -saliva of diabetics without signs of periodontal disease, confirming the hypothesis of immunological implication, as a correlation between periodontal disease incidence and diabetes mellitus. Histologic alterations, suggesting a local inflammatory state, were present in periodontal tissue of diabetics, confirming the above hypothesis. The study reveals that saliva analysis is a quite efficient method in testing the periodontal breakdown progression in the subjects with T2DM.

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