Journal of Clinical and Basic Research (Nov 2022)

Clinico-Immunohistochemical Analysis of Ovarian Tumors in a South Indian Population: A Single-Center Study

  • Venkata Hari Charan Bagadi,
  • Venkata Sunanda Lakshmi Gelli,
  • Vijaya Lakshmi Muram Reddy,
  • Krishna Murthy Badugu

Journal volume & issue
Vol. 6, no. 3
pp. 10 – 16

Abstract

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Background and objectives: In India, ovarian tumors are the fifth leading cause of death in women. They account for 6% of all cancers in women. The present study aimed to provide support for a new theory of ovarian carcinogenesis by investigating the frequency of ovarian tumors and determining whether the Ki-67 labeling index and p53 overexpression help in differentiating borderline and malignant surface epithelial tumors. Methods: The study included all ovarian tumor specimens sent for histopathological examination to the Department of Pathology of Narayana Medical College between June 2017 and October 2019. Results: The frequency of benign epithelial and malignant tumors was 85.47% and 11.97%, respectively. Surface epithelial tumors (81.96%) and germ cell tumors (8.54%) were the most common ovarian tumors. In immunohistochemistry, p53 overexpression in surface epithelial neoplasms showed moderate positivity in all 2 cases of serous carcinomas, while 2 out of 6 mucinous carcinomas cases showed weak positivity. All six cases of mucinous carcinomas showed a Ki-67 labeling index of 26-50%. Serous carcinomas showed a high index of 51%, while mucinous carcinomas had a mean index of 37%. Overexpression of Ki-67 was significantly more common in malignant surface epithelial neoplasms (41.83%) when compared with borderline epithelial neoplasms (27%) (p<0.001). Conclusion: In comparison to borderline serous and mucinous tumors, Ki-67 overexpression is significantly higher common in malignant surface epithelial tumors. Moreover, p53 overexpression is significantly more common in serous carcinoma when compared with borderline serous tumors but not mucinous tumors. Overall, these markers could be beneficial for diagnosing difficult cases and predicting prognosis.

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