Cancers (Feb 2022)

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

  • Jorge Luis Ramon-Patino,
  • Ignacio Ruz-Caracuel,
  • Victoria Heredia-Soto,
  • Luis Eduardo Garcia de la Calle,
  • Bulat Zagidullin,
  • Yinyin Wang,
  • Alberto Berjon,
  • Alvaro Lopez-Janeiro,
  • Maria Miguel,
  • Javier Escudero,
  • Alejandro Gallego,
  • Beatriz Castelo,
  • Laura Yebenes,
  • Alicia Hernandez,
  • Jaime Feliu,
  • Alberto Pelaez-García,
  • Jing Tang,
  • David Hardisson,
  • Marta Mendiola,
  • Andres Redondo

DOI
https://doi.org/10.3390/cancers14040912
Journal volume & issue
Vol. 14, no. 4
p. 912

Abstract

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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.

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