Scholars International Journal of Obstetrics and Gynecology (SIJOG)
Volume-7 | Issue-12 | 671-677
Original Research Article
Evaluation of Prognostic Factors in Patients with Endometrial Cancer
Dr. Suraiya Khanam, Dr. Shamima Akter, Dr. Nasrin Akter, Dr. Rahima Khatun, Dr Md. Sayem Shahriar, Dr. MST. Sharmin Ferdous
Published : Dec. 31, 2024
Abstract
Background: Endometrial cancer (EC) is a leading gynecological malignancy worldwide, with rising incidence in developing countries. Prognostic factors play a critical role in guiding management, especially in resource-limited settings where molecular testing is not routinely available. Aim of the study: To evaluate clinicopathological prognostic factors influencing recurrence in patients with endometrial cancer treated at a tertiary care hospital in Bangladesh. Methods: A retrospective observational study was conducted on 45 histologically confirmed EC patients who underwent hysterectomy-based surgery. Demographic, clinical, pathological, and treatment-related data were analyzed using SPSS version 26.0. Univariate analysis was performed with Chi-square or Fisher’s exact test, and multivariate Cox regression was used to determine independent predictors of recurrence. Hazard ratios (HR) with 95% confidence intervals (CI) were reported, and a p-value <0.05 was considered statistically significant. Result: The mean age was 54.9±12.4 years; 55.6% were postmenopausal. Most patients presented with stage I disease (77.8%). Significant independent predictors of recurrence included tumor grade 3 (HR 2.75; p=0.039), myometrial invasion ≥50% (HR 3.80; p=0.023), LVSI presence (HR 4.25; p=0.007), advanced FIGO stage III–IV (HR 5.67; p=0.004), and lymph node positivity (HR 3.92; p=0.032). Surgical approach and adjuvant therapy were not significantly associated with recurrence. Conclusion: Advanced stage, high tumor grade, deep myometrial invasion, LVSI, and lymph node involvement are key prognostic factors for recurrence in EC. In low-resource settings, reliance on these clinicopathological predictors is essential for risk stratification and optimizing treatment strategies.