Conventional versus giant basal cell carcinoma, a review of 57 Cases: Histologic differences contributing to excessive growth

Background: Giant basal cell carcinoma (GBCC) is defined as a basal cell carcinoma (BCC) exceeding 5 cm in size. While these tumors impart significant morbidity due to local tissue destruction and have a higher rate of metastatic disease than their conventional (smaller) counterparts, reasons for their large size remain unclear. While theories relating to neglect or faster growth rate are often invoked; to date, there has not been a comprehensive evaluation of the histologic features of these large tumors that may contribute to their size. Methods: Histologic features of GBCCs (n = 29) were evaluated and compared to those of conventional BCC (n = 28). Available clinical demographic data were also reviewed. Results: GBCCs, in addition to overall larger size, more often were thicker, displayed ulceration, and showed a more infiltrative growth pattern than their conventional counterparts. These rare tumors also displayed an insignificant increased propensity for a brisk host immune response, and the infiltrate significantly more often included clusters of plasma cells. Conclusions: Most histologic features seen in GBCCs likely reflect their large size. Histologic features alone are unlikely to explain the size of these rare tumors. The possibility of an altered host immune response contributing to the growth of these tumors requires further investigation

Corrigendum: Leukemia cutis as the presenting symptom of acute myeloid leukemia: report of three cases

The original article was published on July19, 2017 and corrected on June 15, 2018.The revised version of the article adds appropriate in-text references to Figures 3B, C and 5B, C, and correctly renumbers the list of References. The changes appear in the revised online PDF copy of this article

Leukemia cutis as the presenting symptom of acute myeloid leukemia: report of three cases

Leukemia cutis (LC), a rare cutaneous manifestation of leukemia, can precede, follow, occur concurrently with, or present in the absence of (aleukemic) systemic leukemia. Leukemia cutis is especially rare as the presenting symptom of leukemia and is associated with a poor prognosis. Although more commonly seen in acute leukemias of myeloid and monocytic lineage, lymphocytic/lymphoblastic leukemias can also involve the skin. Three cases of LC presented with diverse skin lesions ranging from an erythematous rash to violaceous macules and papules to subcutaneous nodules. One case clinically mimicked fixed drug eruption. All the patients had acute myeloid leukemia (AML). Lesions showed two overarching histologic patterns: atypical perivascular infiltrate or nodular dermal histiocytoid infiltrate. Our cases expressed myeloperoxidase (MPO), a helpful marker to distinguish myeloid from non-myeloid cells, and CD68, a monocytic marker frequently expressed in cutaneous AML. CD14, a marker of monocyte maturity, was negative. In the absence of systemic leukemia, common diagnostic tools for hematologic malignancies such as bone marrow biopsy and flow cytometry are non-contributory, making morphologic and immunohistochemical analysis of the skin lesions key to diagnosis

Corrigendum: Leukemia cutis as the presenting symptom of acute myeloid leukemia: report of three cases

The original article was published on July19, 2017 and corrected on June 15, 2018.The revised version of the article adds appropriate in-text references to Figures 3B, C and 5B, C, and correctly renumbers the list of References. The changes appear in the revised online PDF copy of this article

A Case of Syringolymphoid Hyperplasia with Follicular Mucinosis

Syringolymphoid hyperplasia (SLH) is an extremely rare histopathological entity with fewer than 40 cases reported in the literature. SLH have been seen as both benign lesions and in association with T-cell lymphoproliferative lesions. A 20-year-old male presented with a solitary, infiltrated plaque on the left cheek initially diagnosed as a sebaceous carcinoma at an external institution. A repeat biopsy demonstrated prominent follicular mucinosis (FM), squamous metaplasia of the eccrine coils, and a moderately dense perieccrine lymphocytic infiltrate mimicking eccrine carcinoma. The lesion was subsequently diagnosed as SLH with associated FM, an entity that has been previously reported in 12 cases, including this current case. This case highlights the characteristic features of a rare entity, emphasizes the potential for misdiagnosis of SLH, and adds to the current series of SLH described in the literature

Kaposi Sarcoma in Afghanistan: A Case Series from a Tertiary Referral Center

Kaposi sarcoma is a vascular endothelial neoplasm caused by human herpesvirus 8. Although it is a well-studied disease, little is known about the specific characteristics or epidemiology of Kaposi sarcoma in Afghanistan. The data consist primarily of anecdotal reports and epidemiological studies extrapolated from neighboring countries. In this case series, we summarize existing data about Kaposi sarcoma in Afghanistan and present seven histologically confirmed cases with associated clinical features to shed light on the characteristics of Kaposi sarcoma in this unique geographic setting

Utility of artificial intelligence in a binary classification of soft tissue tumors

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities