Menopausal Status Contributes to Overall Survival in Endometrial Cancer Patients

SIMPLE SUMMARY: Endometrial cancer is a leading cause of female genital tract malignancies, and its incidence has been rising in recent years. Despite relatively good prognosis, a noticeable number of patients eventually die within five years of diagnosis. Thus, one of the most common questions addressed to oncologists by patients and their families refers to prognosis. Our study was performed to assess the influence of some conventional clinicopathological features on overall (all-cause) survival of endometrial cancer patients. We have proved that some characteristics related to menopause status (i.e., time from last menstruation and postoperative FSH concentration) may strongly correlate with patients’ survival. Further research on various risk factors will provide us with improved and more personalized care for cancer patients. ABSTRACT: Endometrial cancer is the most common female genital tract malignancy in developed countries that occurs predominantly in postmenopausal women. The primary objective of our research was to investigate whether menopause status together with selected conventional prognostic indicators may contribute to overall (all-cause) survival in endometrial cancer patients. For this purpose, we applied the Cox proportional hazards regression model. Patients in advanced FIGO stage showed a relatively poor survival rate. The time since last menstruation and postoperative FSH concentration were identified as unfavorable prognostic factors in our model. Additionally, age at diagnosis, BMI value, adjuvant treatment (brachytherapy), and parity showed no impact on survival. To our knowledge, this is the first study to report a prognostic model for endometrial cancer including exact time from last menstruation as one of the prognostic variables. Due to the fact that there are no stratifying systems to reliably predict survival in patients with endometrial cancer, there is a strong need to revise and update existing models using complementary prognostic indicators. Collection of precise data on various risk factors may contribute to increased accuracy of artificial intelligence algorithms in order to personalize cancer care in the near future.