Endometrial Cancer International Database

Overview

This project aims to determining prognostic factors and individualizing management decision per patient characteristics and endometrial cancer features. This study will include at least 10 centers from different countries that present at least Europe, South America, Asia, and Africa. Data will be retrospectively collected from January 2008 to December 2015 with a total follow-up of at least 5 years (December 2020).

Endometrial cancer (EC) or carcinoma of corpus uteri is a neoplastic change, mostly adenocarcinoma, arising from uterine columnar epithelial lining. Abnormal uterine bleeding is the main presenting symptom especially in postmenopausal women. EC is estimated to be the seventh most commonly diagnosed cancer in women. It is considered the 16th leading cause of death in women with cancer worldwide, with 382 000 estimated new cases and 89 900 deaths in 2018. Factors as obesity, parity, diabetes mellitus, unopposed estrogen exposure, genetics and hormonal therapies are recognizable risks for EC. Other factors as chronic comorbidities, tumor size and organ metastasis influence staging, prognosis, and management protocols. FIGO staging has been adopted as the standard classification system in the management of endometrial cancer. However, this staging system does not consider all factors that affect treatment decision and prognosis including but not limited to patient demographics, tumor grade, and lymphovascular space invasion. In addition, some interventions are still debatable particularly in the presence of intermediate disease e.g, grade II early EC. Available evidence supports combined pelvic and para-aortic lymphadenectomy in management of patients with EC. However, combined pelvic and para-aortic lymphadenectomy carry the risk of long term morbidities as lymphedema. Thus, hysterectomy alone as management plan is suggested in patients with low risk EC. More comprehensive studies of the multiple confounders that determine patient's risk are essential to reach an individually adjusted management plans and to predict prognosis of each individual case. Therefore, availability of large multicenter studies will provide robust evidence regarding optimal management of EC and hence, improve treatment outcome and prognosis particularly in the era of machine learning and artificial intelligence.

Conditions

Eligibility

Outcomes

Central Contacts