Litcius/Paper detail

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

Jorge Luis Ramón-Patiño, Ignacio Ruz‐Caracuel, Victoria Heredia-Soto, L.E. García de la Calle, Bulat Zagidullin, Yinyin Wang, Alberto Berjón, Álvaro López‐Janeiro, María P. De Miguel, Javier Escudero, Alejandro Gallego, Beatriz Castelo, Laura Yébenes, Alicia Hernández, Jaime Feliú, Alberto Peláez‐García, Jing Tang, David Hardisson, Marta Mendiola, Andrés Redondo

2022Cancers10 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Risk stratificationEndometrial cancerOncologyClassifier (UML)MedicineInternal medicineDiagnostic accuracyMolecular biomarkersPrecision medicineBioinformaticsArtificial intelligenceCancerPathologyComputer scienceBiologyEndometrial and Cervical Cancer TreatmentsGenetic factors in colorectal cancerCancer-related molecular mechanisms research