BackgroundThere has been an increase in the use of fine needle aspiration cytology (FNAC) for the diagnosis of parathyroid lesions (PLs). Differentiation between a thyroid lesion and a PL is not easy because of their similar features. We reviewed parathyroid aspirates in our institution and aimed to uncover trends in diagnostic criteria.MethodsWe selected 25 parathyroid aspirates (from 6 men and 19 women) confirmed surgically or immunohistochemically from 2006 to 2011.ResultsMajor architectural findings of PLs include scattered naked nuclei, loose clusters, a papillary pattern with a fibrovascular core, tight clusters, and a follicular pattern. These architectures were commonly admixed with one another. Cytological features included anisokaryosis, stippled chromatin, a well-defined cell border, and oxyphilic cytoplasm. Eighteen of the 25 patients were diagnosed with PL using FNAC. Seven patients had been misdiagnosed with atypical cells (n=2), benign follicular cells (n=2), adenomatous goiter (n=2) and metastatic carcinoma (n=1) in FNAC. Using clinicoradiologic data, the sensitivity of the cytological diagnosis was 86.7%. The cytological sensitivity decreased to 50% without this information.ConclusionsFNAC of PL is easily confused with thyroid lesions. A combination of cytological parameters and clinical data will be required to improve the diagnostic sensitivity of PLs.
Purpose: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool. Experimental Design: Using 2,434 whole-slide images, we developed an algorithm based on convolutional neural networks to classify a gastric biopsy image into one of three categories: negative for dysplasia (NFD), tubular adenoma, or carcinoma. The performance of the algorithm was evaluated by using 7,440 biopsy specimens collected prospectively. The impact of algorithm-assisted diagnosis was assessed by six pathologists using 150 gastric biopsy cases. Results: Diagnostic performance evaluated by the AUROC curve in the prospective study was 0.9790 for two-tier classification: negative (NFD) versus positive (all cases except NFD). When limited to epithelial tumors, the sensitivity and specificity were 1.000 and 0.9749. Algorithm-assisted digital image viewer (DV) resulted in 47% reduction in review time per image compared with DV only and 58% decrease to microscopy. Conclusions: Our algorithm has demonstrated high accuracy in classifying epithelial tumors and its benefits as an assistance tool, which can serve as a potential screening aid system in diagnosing gastric biopsy specimens.
BackgroundPleomorphic carcinoma (PC) is a rare pulmonary malignancy. Because of its rarity and histological heterogeneity, cytopathologists might suspect PC only rarely on the basis of its cytological specimen. In addition, cytological findings from fine needle aspiration (FNA) specimens have rarely been described. Hence, we investigated the cytological features of FNA in the cases of PC.MethodsWe reviewed 7 FNA specimens of PC. The patients had undergone surgical resection at the Korea Cancer Center Hospital between 2007 and 2011. The cytological features of PC were assessed and compared with the histopathological features of the corresponding surgical specimen. Immunocytochemical analysis with cytokeratin and vimentin was performed on the cell blocks.ResultsThe tumor cells were either dispersed or arranged in loose aggregates, and generally lacked any glandular or squamous differentiation. Pleomorphic or spindle shape tumor cells were observed, and mono-, bi-, or multi-nucleated giant cells were frequently observed. The background showed necrosis and contained numerous lymphocytes and neutrophils. Immunocytochemically, the tumor cells were positive for cytokeratin and vimentin.ConclusionsPC displays characteristic cytological features. It might therefore be possible to make an accurate diagnosis of PC by assessing the degree of nuclear atypia.
Thus, we found that activation of Notch signalling was correlated significantly with clinicopathological parameters. Therefore, Notch signalling could be a useful prognostic marker in patients with PTC.
PurposeThe Notch signaling pathway is widely expressed in normal, reactive, and neoplastic tissues; however, its role in thyroid tissues has not been fully elucidated. Therefore, this study was conducted to characterize the expression of the Notch signaling pathway in papillary thyroid cancer (PTC) cells and anaplastic thyroid cancer (ATC) cells.Materials and MethodsExpression of activated Notch1 in ATC and PTC paraffin-embedded tissues was determined by immunohistochemistry. The small interfering RNA techniquewas employed to knock down Notch1 expression in ATC and PTC cell lines.ResultsThe expression of activated Notch1 was higher in ATC cases than in PTC cases. Inhibition of Notch1 significantly reduced proliferation and migration of ATC cells, but not PTC cells. In addition, inhibition of Notch1 in ATC cells significantly reduced the expression of key markers of epithelial-mesenchymal transition and cancer stem cells. Conversely, changes in the expression of these proteins were not observed in PTC cells.ConclusionThe results of this study suggest that Notch1 expression plays different roles in tumor progression in ATC and PTC cells. We also found that Notch1 expression was significantly related to the highly invasive or proliferative activity of ATC cells.
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