Context:Fine needle aspiration (FNA) plays a crucial role in the evaluation of patients with thyroid lesions. The Bethesda system for reporting thyroid cytopathology (TBSRTC) was designed with a mission to standardize the process of diagnosis and management of thyroid lesions by FNA cytology (FNAC).Aim:We aim to see the benefits of adopting TBSRTC, seek the cytological pitfalls in the diagnosis of thyroid FNAC, and identify the spectrum of thyroid lesions in our setup.Settings and Design:This is a hospital-based cross-sectional study conducted from June 2009 to June 2014 of all thyroid FNACs with available histopathology reports. Cases were designated a specific diagnostic category according to TBSRTC.Materials and Methods:A total of 109 cases were included in the study. Sixty-eight cases had been reported without using TBSRTC and were reviewed and reclassified according to TBSRTC seeking the common reasons for interpretative errors.Statistical Analysis Used:Data were analyzed using SPSS ver. 11.5.Results:In both pre- and post-TBSRTC era, benign neoplasms constituted the major bulk. After the use of TBSRTC, there was increased ability to look for follicular neoplasms, improvement in making definitive diagnosis of the cases, decline in the suspicious category, and an improvement in diagnostic accuracy, and we were in line with the implied risk outlined by TBSRTC in most of the cases except the nondiagnostic or unsatisfactory category.Conclusion:Application of TBSRTC results in uniformity in reporting among pathologists and better interdisciplinary communication and patient management.
Ovarian hemangiomas are uncommon benign vascular tumors of ovary. Most of them are asymptomatic and detected incidentally during surgery. Authors report a case of 41 years female, parity 2; with complain of lower abdominal pain for 6 months. Ultrasonography showed a cystic lesion at right adnexa with a heterogeneously echogenic component within and devoid of internal vascularity. Laparoscopic right adnexal cystectomy was done, which on histopathological examination demonstrated features of cavernous hemangioma replacing the ovarian parenchyma. As surgical excision is treatment of choice, correct diagnosis is essential to avoid unnecessary radical surgery and treatment.
Collision tumors are composed of two histologically distinct neoplasms in the same organ without intermixture of cell types. The co-occurrence of a serous adenocarcinoma with a mature cystic teratoma is very rare. We present here the case of a 48 years married female with bilateral high grade serous carcinoma of the ovary with left sided mature cystic teratoma.Asian Journal of Medical Sciences Vol.9(4) 2018 61-64
Introduction: Frozen section helps in rapid intra-operative diagnosis. It is commonly used during surgical procedures to detect malignancy so that modifications of surgery can be decided at the time of surgery on the table. Frozen section is also performed for evaluation of surgical margins and detection of lymph node metastasis. In addition it is applied for detection of unknown pathological processes.The objective of this study was to assess the accuracy of frozen section diagnosis in comparison to gold standard histopathological diagnosis and to find concordance and discordance rate of frozen section with histopathological report.Methods: This was a cross sectional study of 41 frozen section samples done in the department of pathology of BP Koirala Institute of Health Sciences from September 2014 to August 2015. All frozen section samples with their permanent tissue samples sent for final histopathological evaluation were included in the study.Results: The overall accuracy of frozen section diagnosis was 97%. The sensitivity was 94%, specificity was 87%, positive predictive value was 90% and negative predictive value was 93%. The concordance rate was 90.2% and the discordance rate was 9.8%.Conclusions: The results of frozen section varied in different organ systems and the common cause of discrepancy in our study were the gross sampling error and the interpretational error.
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