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Rev Esp Patol
January 2025
Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
Background: Sarcoidosis, a granulomatous inflammatory disease, exhibits diverse clinical manifestations, often affecting multiple organs. Diagnostic challenges arise due to its similarities with tuberculosis, particularly in high-burden areas. Differentiating between the two relies on clinical judgment, laboratory tests, imaging, and invasive procedures.
View Article and Find Full Text PDFJ Am Soc Cytopathol
November 2024
Cytopathology Center of Excellence, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
Introduction: Renal cell carcinoma (RCC) involves serosal surfaces in 2%-3% of cases, and thus few papers describe serous fluid cytology (SFC) involvement by RCC. This diagnosis is challenging, given its rarity, nondescript cytomorphologic features and infrequent expression of widely used epithelial markers MOC31 and BerEP4. We describe our institutional experience with RCC in SFC specimens.
View Article and Find Full Text PDFCancer Cytopathol
January 2025
Department of Pathology, Pathology & Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Oncol Lett
January 2025
Institute of Pathology, Faculty of Medicine, University of Belgrade, Belgrade 11000, Serbia.
Lung cancer is among the lethal and most prevalent oncological diseases globally. It is known that two types of mutations, namely anaplastic lymphoma kinase (ALK) gene rearrangement and epidermal growth factor receptor (EGFR) gene mutation, are responsible for the development of lung adenocarcinoma. The present study aimed to investigate the differences in the frequency of clinical, cytomorphological and histomorphological features of ALK and EGFR-positive lung adenocarcinomas.
View Article and Find Full Text PDFAm J Pathol
February 2025
State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 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 gland 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, was developed to accurately classify the most prevalent subtypes of SGNs.
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