Seborrheic keratosis (SK), actinic keratosis (AK), and Bowen's disease (BD) are squamoproliferative disorders of the skin. Histologically, they may mimic each other and therefore, they might be misinterpreted, especially in small samples. The aim of this study is to clarify the expression of p63, p16, and p53 proteins in SK, AK, and BD and evaluate the efficacy of these markers in order to distinguish between the aforementioned lesions. A total of 46 cases were collected (15 SK, 16 AK, and 15 BD) and stained for p63, p16, and p53. The stain intensity and the cell distribution labeling were scored and then analyzed by SPSS software. All cases of BD which became positive for p53 revealed basal keratinocytes sparing. Instead, all or nearly all basal keratinocytes in AK cases were positive for this marker. These were also seen in p16 staining results and they were between AK and BD (P = .024). Our study demonstrates p16 and p53 are useful markers in separating AK and BD according to basal keratinocytes involvement and sparing, respectively.

Background & Objective: Predicting the transformation of dysplastic or congenital nevi into malignant lesions results in a significant increase in the survival of patients. Some specific gene mutations have been reported to be very helpful in this regard. Therefore, this study aimed to evaluate the prevalence of BRAF V600E mutation in dysplastic and congenital nevi. Methods: This cross-sectional study was conducted on patients with congenital (n=30) or dysplastic (n=30) nevi. For genomic analysis, the BRAF gene mutation (V600E) was evaluated using the real-time polymerase chain reaction. Results: The prevalence of BRAF gene (V600E) mutation was found as 1 case (3.3%) in congenital and 8 cases (26.7%) in dysplastic nevi indicating the higher prevalence of this mutation in patients with dysplastic nevi ( P =0.026). Moreover, in the dysplastic nevi group, the presence of BRAF gene mutation (V600E) showed a significant relationship with the severity of dysplasia as the mutation rate was 25% in mild cases, in comparison with 54.5% in moderate dysplasia cases ( P =0.009). Conclusion: According to the results, 3.3% of the patients with congenital nevi and 26.7% of the subjects with dysplastic nevi were positive for BRAF V600E mutation. Furthermore, the severity of dysplasia could have a positive relationship with the presence of the mutation.

Background: This observational study aimed to describe and compare histopathological, architectural, and nuclear characteristics of sebaceous lesions and utilized these characteristics to develop a predictive classification approach using machine learning algorithms. Methods: This cross-sectional study was conducted on patients with sebaceous from March 2015 to March 2019. Pathology slides were retrieved and reviewed. Two distinct pathologists assessed each slide regarding architectural and cytological attributes. A decision tree method was used to develop a prediction model. multiple models were trained on a random 80% train set, this time only using the selected variables, and mean accuracy was calculated. Results: This study assessed characteristics of 124 sebaceous tumors. Histopathological findings such as pagetoid appearance, neurovascular invasion, atypical mitosis, extensive necrotic area, poor cell differentiation, and non-lobular tumor growth pattern, as well as nuclear features such as highly irregular nuclear contour, and large nuclear size were exclusively observed in carcinomatous tumors. Among non-carcinomatous lesions, some sebaceoma cases had features like infiltrative tumor margin, and high mitotic activity which can be misleading and complicate diagnosis. Based on multiple decision tree models, the five most critical variables for lesion categorization were identified as: nuclear contour, nucleoli, peripheral basaloid cell layers, basaloid cell count, and chromatin. Conclusions: This study implemented a machine learning modeling approach to help categorize controversial sebaceous lesions based on architectural and nuclear features, optimally. However, studies of larger sample sizes are needed to ensure the accuracy of our suggested predictive model.