Histopathological pattern and associated factors of ovarian tumors at University of Gondar comprehensive specialized Hospital, Northwest Ethiopia: a retrospective study, 2021

Abstract

Abstract Introduction: Ovarian tumor is a heterogeneous group of neoplasm with abnormal growth of tissue on or in a woman's ovary. It is one of the most common gynecological tumors in the world, with a wide range of histological variations. It can be benign, borderline, or malignant, with benign tumors accounting for around 80% of all ovarian tumors. As of the 2020 statistics, there were 312, 800 new cases and 206,800 new ovarian cancer-related deaths worldwide. Africa shares 3.8% of the new cases and 4.4% of the new death. In Ethiopia, ovarian cancer is the 3rd leading cause of cancer-related deaths among women, with approximately 2,655 diagnosed cases and 1,889 deaths in 2020. Plenty of risk factors are attributable for the increment in the burden of ovarian cancer, including age, infertility, early age at menarche, late age at menopause, advanced age at first birth, less breastfeeding, low parity, family history of ovarian, breast, endometrioid and colorectal cancer. Understanding the pattern and risk factors for different ovarian tumor histologies can help us to better care for patients and provide tumor-specific interventions. Objective: The study aimed to assess the histopathological pattern and associated factors of ovarian tumors among patients treated at University of Gondar comprehensive specialized hospital. Methods: Institution-based retrospective cross-sectional study design was conducted. The study included 218 ovarian tumor patients who were treated at University of Gondar comprehensive specialized hospital between September 12, 2015, and September 11 2020 were included in the study. Structured questionnaire was used to collect the data by reviewing the patient's chart. The data was then entered into EPI data and exported to STATA. Descriptive statistics like mean, standard deviation, median, interquartile range, and percentage were executed based on the nature of data after checking normal distribution. The data was presented with narration, tabulation, and graphical presentation. Multinomial logistic regression was used to assess the association between the outcome and predictor variables. The significance level was set at P<0.05 with 95 % Confidence Interval (CI)