Prognosis Stratification Tools in Early-Stage Endometrial Cancer: Could We Improve Their Accuracy?

Simple Summary Endometrial cancer is the most common gynaecological malignancy in developed countries. Most cases are diagnosed at a localized stage, overall with a good prognosis, although approximately 15% of them will recur. The identification of patients with an increased risk of relapse remains a challenge for clinicians. There are well-defined clinicopathological characteristics associated with prognosis. These variables have been integrated in multiple classifiers to stratify the prognosis, and more recently, molecular features have also been considered. The aim of our retrospective study was to compare the three available prognostic stratification tools for endometrial cancer and determine if additional biomarkers could improve their accuracy. We confirmed that the incorporation of molecular classification in risk stratification resulted in better discriminatory capability, which was improved even further with the addition of CTNNB1 mutational evaluation. There are three prognostic stratification tools used for endometrial cancer: ESMO-ESGO-ESTRO 2016, ProMisE, and ESGO-ESTRO-ESP 2020. However, these methods are not sufficiently accurate to address prognosis. The aim of this study was to investigate whether the integration of molecular classification and other biomarkers could be used to improve the prognosis stratification in early-stage endometrial cancer. Relapse-free and overall survival of each classifier were analyzed, and the c-index was employed to assess accuracy. Other biomarkers were explored to improve the precision of risk classifiers. We analyzed 293 patients. A comparison between the three classifiers showed an improved accuracy in ESGO-ESTRO-ESP 2020 when RFS was evaluated (c-index = 0.78), although we did not find broad differences between intermediate prognostic groups. Prognosis of these patients was better stratified with the incorporation of CTNNB1 status to the 2020 classifier (c-index 0.81), with statistically significant and clinically relevant differences in 5-year RFS: 93.9% for low risk, 79.1% for intermediate merged group/CTNNB1 wild type, and 42.7% for high risk (including patients with CTNNB1 mutation). The incorporation of molecular classification in risk stratification resulted in better discriminatory capability, which could be improved even further with the addition of CTNNB1 mutational evaluation.