Adenoid basal hyperplasia is an underrecognized cervical lesion, resembling adenoid basal carcinoma, except the absence of deep invasion into the stroma. We report a series of 10 cases, all extending less than 1 mm from the basement membrane. Our results support the hypothesis that adenoid basal hyperplasia arises from reserve cells of the cervix. Lesions were found close to the squamocolumnar junction, in continuity with the nearby subcolumnar reserve cells. They shared the same morphology and immunoprofile using a panel of 4 antibodies (keratin 5/6, keratin 14, keratin 7 and p63) designed to differentiate reserve cells from mature squamous cells and endocervical columnar cells. We detected no human papillomavirus infection by in situ hybridization targeting high-risk human papillomavirus, which was concordant with the absence of immunohistochemical p16 expression. We demonstrated human papillomavirus infection in 4 (80%) of 5 adenoid basal carcinoma, which is in the same range as previous studies (88%). Thus, adenoid basal hyperplasia should be distinguished from adenoid basal carcinoma because they imply different risk of human papillomavirus infection and of subsequent association with high-grade invasive carcinoma. In our series, the most reliable morphological parameters to differentiate adenoid basal hyperplasia from adenoid basal carcinoma were the depth of the lesion and the size of the lesion nests. Furthermore, squamous differentiation was rare in adenoid basal hyperplasia and constant in adenoid basal carcinoma. Finally, any mitotic activity and/or an increase of Ki67 labeling index should raise the hypothesis of adenoid basal carcinoma.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.humpath.2012.03.023 | DOI Listing |
Introduction: Thyroid and salivary gland cytopathology frequently present diagnostic challenges due to complex presentations, overlapping features between benign and malignant conditions, particularly with grey-zone entities and rare pathologies. To address these issues, the 45th European Congress of Cytology (ECC) held a slide seminar focused on challenging cases. This article reviews key findings from the six cases discussed, emphasizing the importance of a comprehensive diagnostic approach.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
December 2024
Department of Pathology, Shanghai Cancer Center, Fudan University and Department of Oncology, Shanghai medical College, Fudan University, Shanghai200032, China.
Diagn Cytopathol
December 2024
Department of Pathology and Laboratory Medicine, Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra, Greenvale, New York, USA.
Introduction: In this study we aim to analyze the TRPS1 immunostaining of salivary gland tumors (SGT) on cytology cell blocks and compare the staining pattern on subsequent surgical resections.
Methods: Malignant SGTs, oncocytomas and basal cell adenomas diagnosed on fine needle aspiration were retrieved from 2019 to 2021 database. Cases with surgical follow-up were selected.
Ann Diagn Pathol
February 2025
Department of Pathology, Albany Medical Center, Albany Medical College, Albany, NY, USA.
Recent studies suggest that trichorhinophalangeal syndrome type 1 (TRPS1) is sensitive immunomarker for breast carcinoma (BC). Salivary duct carcinoma (SDC) of salivary gland can share similar morphologic and immunophenotypic features with BC. This study aimed to assess the expression of TRPS1 in SDC and other salivary gland tumors (SGTs).
View Article and Find Full Text PDFAm J Pathol
October 2024
State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, China. Electronic address:
Salivary gland neoplasms (SGNs) represent a group of human neoplasms characterized by a remarkable cytomorphologic diversity, which frequently poses diagnostic challenges. Accurate histologic categorization of salivary tumors is crucial to make precise diagnoses and guide decisions regarding patient management. Within the scope of this study, a computer-aided diagnosis model using Vision Transformer (ViT), a cutting-edge deep learning model in computer vision, has been developed to accurately classify the most prevalent subtypes of SGNs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!