Background: Extranodal extension (ENE) in lymph node metastases has been shown to worsen the prognosis of papillary thyroid cancer (PTC). Despite the clinical significance of ENE, there are no stringent criteria for its microscopic diagnosis, and its identification is subject to inter-observer variability. The objective of this study was to determine the level of agreement among expert pathologists in the identification of ENE in PTC cases.
Methods: Eleven expert pathologists from the United States, Italy, and Canada were asked to review 61 scanned slides of representative permanent sections of PTC specimens from Mount Sinai Beth Israel Medical Center in New York. Each slide was evaluated for the presence of ENE. The pathologists were also asked to report the criteria they use to identify ENE.
Results: The overall strength of agreement in identifying ENE was only fair (κ = 0.35), and the proportion of observed agreement was 0.68. The proportions of observed agreement for the identification of perinodal structures (fat, nerve, skeletal, and thick-walled vessel involvement) ranged from 0.61 to 0.997.
Conclusions: Overall agreement for the identification of ENE is poor. The lack of agreement results from both variation in pathologists' identification of features and disagreement on the histologic criteria for ENE. This lack of concordance may help explain some of the discordant information regarding prognosis in clinical studies when this feature is identified.
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http://dx.doi.org/10.1089/thy.2015.0551 | DOI Listing |
J Clin Med
December 2024
Department of General Pathology and Pathologic Anatomy, Faculty of Medicine, University of Rijeka, Braće Branchetta 20, 51000 Rijeka, Croatia.
: In this paper, we investigate the association of glycoprotein 96 (GP96) and androgen receptor (AR) expression with clinicopathological factors, additional axillary lymph node burden, and their potential role in predicting 5-year overall survival (OS) and disease-free survival (DFS) in breast cancer (BC) patients with sentinel lymph node (SLN) involvement. We also explore the prognostic value of the presence of extranodal extension (ENE) in SLN. : We retrospectively enrolled 107 female patients with cT1-T2 invasive BC and positive SLN biopsy.
View Article and Find Full Text PDFMedeni Med J
December 2024
Dokuz Eylül University Faculty of Medicine, Departmet of Medical Pathology, İzmir, Türkiye.
Objective: Angiotropism/perivascular invasion (PVI) is an emerging topic in various types of cancer, with studies primarily focusing on melanoma. However, limited data are available on the significance of PVI in breast cancer. This study aimed to assess the prognostic significance of PVI in breast cancer and its correlation with traditional clinicopathological prognostic parameters.
View Article and Find Full Text PDFOtolaryngol Head Neck Surg
December 2024
Department of Otolaryngology-Head and Neck Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Objectives: We investigate if sublingual space invasion (SLI) determined on magnetic resonance imaging confers differences in clinicopathological manifestations and treatment outcomes of oral tongue squamous cell carcinoma (OTSCC).
Study Design: Retrospective cohort study.
Setting: Tertiary Academic Medical Center.
Cancer Med
December 2024
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Background: To investigate the impact of the number of positive lymph nodes (PLNs) on long-term survival and pathological nodal stage in patients with oral tongue squamous cell carcinoma (OTSCC).
Materials And Methods: Newly diagnosed and nonmetastatic adult patients with OTSCC who underwent curative resection were identified between January 2010 and December 2020. External validation was performed via the SEER registry.
Indian J Radiol Imaging
January 2025
Department of Radiology, Dr. Bhim Rao Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences New Delhi, India.
The aim of this study was to assess efficacy of large language models (LLMs) for converting free-text computed tomography (CT) scan reports of head and neck cancer (HNCa) patients into a structured format using a predefined template. A retrospective study was conducted using 150 CT reports of HNCa patients. A comprehensive structured reporting template for HNCa CT scans was developed, and the Generative Pre-trained Transformer 4 (GPT-4) was initially used to convert 50 CT reports into a structured format using this template.
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