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http://dx.doi.org/10.1007/s00417-011-1836-0 | DOI Listing |
Pak J Med Sci
December 2024
Dr. Asif Bashir, MD, FAANS, FACS Professor of Neurosurgery, Department of Neurosurgery Unit-I, Punjab Institute of Neurosciences, Lahore, Pakistan.
Objectives: To evaluate the precision and safety of a novel technique of free-hand frameless pinless AXIEM™-based navigation guided biopsy of deep-seated brain lesions in a low-middle income country.
Methods: This retrospective study included 45 patients who underwent free-hand frameless pinless AXIEM™-based navigation guided biopsy of deep-seated brain lesions using the Medtronic-Stealth S7 system over a 5-year period (January 2019 to December 2023) at the Department of Neurosurgery, Punjab Institute of Neurosciences, Lahore, Pakistan.
Results: A total of 45 patients were included in this study.
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 PDFIn Vivo
October 2024
Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea;
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