Cytopathology最新文献

Granular cell tumours (GCT) are rare neoplasms and were first described in 1926 by Abrikossoff. These tumours occur in adults and are reported mainly in oral mucosae, skin and subcutaneous tissue. Herein, we present a case of GCT in the submandibular region in a 10 year old girl. Fine needle aspiration cytology (FNAC) can be challenging, as there can be many overlapping features with other entities, especially if located in an unusual location. We present an atypical presentation with cytohistological correlation.

Atypical spindle cell/pleomorphic lipomatous tumour (AS/PLT) is a novel entity described under the benign lipomatous neoplasm recognised by the recent WHO classification of soft tissue tumours (fifth edition). In this report, we present a case of a 44-year-old male who with a swelling on dorsum of the hand measuring 8 × 8 cm. A Fine needle aspiration cytology (FNA) was performed which revealed adipocytic fragments with individual cells exhibiting mild to moderate nuclear atypia with floret like cells, multinucleated giant cells along with few pseudo-lipoblast-like cells. Based on cytomorphological findings a possibility of an Atypical spindle cell/Pleomorphic lipomatous tumour [AS/PLT] was given. An excision was performed which revealed features confirmed the diagnosis of an AS/PLT. MDM-2 Immunohistochemistry was performed and was negative ruling out a well differentiated Liposarcoma. Hence, the diagnosis was confirmed on histopathological examination. AS/PLT is a unique novel benign neoplasm with a slightly higher recurrence rate than a Pleomorphic lipoma. Here, we discussed the characteristic cyto-morphological features of an AS/PLT that help in the diagnosis and guide further surgical management.

Introduction: Medullary thyroid carcinoma (MTC) is a rare malignant tumour of the thyroid gland that originates from parafollicular cells. Although rare, MTC is aggressive, so early detection is important for improving prognosis. The accuracy of fine-needle aspiration cytology (FNAC) for diagnosing MTC is still controversial.

Methods: The data of 20 patients who underwent thyroid FNAC with histological follow-up to diagnose MTC between 2016 and 2024 were retrospectively collected from The First Affiliated Hospital of Soochow University. Cytological findings, histological features, and serological testing were reviewed, and causes of misdiagnosis were evaluated.

Results: The diagnostic accuracy of FNAC was 90% (18/20). Two patients were initially misdiagnosed with suspected papillary thyroid carcinoma, while eighteen patients were accurately diagnosed in the first instance with suspected MTC. Cytopathological analysis revealed plasmacytoid and spindle-like characteristics with 'salt and pepper' chromatin distributed in the nuclei, with occasional giant tumour cells and frequent deposition of pink-stained amyloid in the background. In addition, serum calcitonin and carcinoembryonic antigen were elevated to varying degrees.

Conclusion: Fine-needle aspiration is a highly valuable method for the preoperative diagnosis of MTC.

Background: Endoscopic ultrasonography-guided fine needle aspiration (EUS-FNA) is widely used for diagnosing pancreatic neoplasms, contributing to improved therapeutic strategies for pancreatic cancer. This aim of this study is to evaluate the diagnostic utility, sample adequacy, and limitations of EUS-FNA cytology for pancreatic lesions. These were further confirmed after surgical resection and histopathological evaluation.

Methods: We retrospectively included patients referred to two Teaching Hospitals in southern Iran for EUS-FNA due to suspected pancreatic cancer, who later underwent surgical resection or biopsy and eventually had a histopathological confirmed diagnosis. The cytological smears from EUS-FNA were compared with the final histology reports to assess diagnostic performance.

Results: Thirty patients were included in the final analysis. EUS-FNA cytology showed sensitivity of 90.00% [95% CI: 68.30, 98.77], specificity of 80.00% [95% CI: 44.39, 97.48], negative predictive value (NPV) of 80.00% [95% CI: 50.89, 93.92], positive predictive value (PPV) of 90.00% [95% CI: 72.09, 96.91], and overall accuracy of 86.67% [95% CI: 69.28, 96.24]. Two false-positive diagnoses (hydatid cyst and chronic pancreatitis) and two false-negative diagnoses (malignancies attributed to sampling errors) were observed. Immunohistochemical tests confirmed 8 out of 9 diagnoses made by cytology. Notably, all EUS-FNA samples provided adequate material for conclusive diagnosis.

Conclusion: These results support the high importance of the diagnostic performance of EUS-FNA on solid pancreatic lesions, even without Rapid On Site Evaluation (ROSE), given that the sample is of adequate size for testing. Despite a few false negative and false positive diagnoses, EUS-FNA should be considered the first-line diagnostic tool for pancreatic lesions assessment.

Objective: The Paris System for Reporting Urinary Cytology (TPS) has clearly and simply identified the criteria for the accurate diagnosis of high-grade urothelial carcinoma (HGUC); however, the urine cytological findings of secondary tumours have not been fully assessed. Therefore, we evaluated the urine cytological features of colorectal adenocarcinoma cells that invaded the urinary tract.

Methods: Thirty urine cytology specimens with cells considered colorectal adenocarcinoma were retrieved by clinicopathological review, subject to the following criteria: no urothelial carcinoma history, diagnosed as HGUC by TPS and supported by CDX-2 immunocytochemistry. Cases were morphologically re-evaluated.

Results: CDX-2 immunostaining, performed in four available cases, showed nuclear positivity in all cases. Tall columnar carcinoma cells were identified in 12/30 cases, tumour cells with pale or vacuolated cytoplasm in 4/30, large and eosinophilic nucleoli in most atypical cells in 4/30, large cells simulating HGUC cells in 27/30 and small cells with irregularly shaped or crescentic nuclei in 27/30. A histological review of transurethral or colorectal resection specimens revealed morphologically identical small cells on the surface of all well-to-moderately differentiated adenocarcinomas, demonstrating desquamated and degenerated adenocarcinoma cells. One cytological specimen from a poorly differentiated adenocarcinoma showed dispersed isolated tumour cells with uniform and round nuclei containing finely granular chromatin and prominent nucleoli.

Conclusions: Colorectal adenocarcinoma cells in the urine may not only be large and tall columnar or vacuolated, but can also simulate HGUC cells or have small round or crescentic nuclei. Recognising this morphological diversity is important for accurate diagnosis.

Kimura disease is uncommon in children below 10 years of age. This article describes the cytologic features on Fine needle aspiration cytology (FNAC) which can serve as soft pointers towards the diagnosis. The diagnosis can further be supported by relevant blood investigations, such as eosinophilia and elevated serum IgE levels.

Background: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) provides a standardised framework for thyroid fine needle aspiration cytology (FNAC). Deep learning approaches, particularly transfer learning, have shown potential for cytology but are rarely applied to TBSRTC categorisation.

Aims and objectives: To evaluate the performance of an ensemble soft voting transfer learning model in categorising thyroid cytology smears according to TBSRTC.

Materials and methods: A retrospective study of 94 thyroid FNAC cases with 949 representative images was conducted. Six transfer learning models (Xception, ResNet50V2, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB3) were combined using ensemble soft voting with a weighted average. Model performance was assessed using sensitivity, specificity, precision (PPV), negative predictive value (NPV), F1 score and area under the receiver operating characteristic curve (AUCROC).

Results: DenseNet121 achieved the highest overall sensitivity (0.91) and specificity (0.98) among all single models. The weighted ensemble model achieved an F1 score of 0.867, marginally below DenseNet121 but with superior precision (0.882) and NPV (0.968). AUCROC was highest in DenseNet 121 (0.99) followed by the weighted ensemble (0.98).

Conclusion: This is the first study to apply ensemble transfer learning to TBSRTC categorisation. The approach demonstrated strong predictive accuracy, particularly for benign and malignant categories. Larger datasets, ideally with whole-slide imaging, are needed to further validate these findings.

A 59-year-old male presented with left upper abdominal pain and weight loss for 3 months. Cytomorphology of a pancreatic neoplasm has been highlighted.