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Prognosis Stratification Tools in Early-Stage Endometrial Cancer: Could We Improve Their Accuracy?
Ramon-Patino, Jorge Luis; Ruz-Caracuel, Ignacio; Heredia-Soto, Victoria; Garcia de la Calle, Luis Eduardo; Zagidullin, Bulat; Wang, Yinyin; Berjon, Alberto; Lopez-Janeiro, Alvaro; Miguel, Maria; Escudero, Javier; Gallego, Alejandro; Castelo, Beatriz; Yebenes, Laura; Hernandez, Alicia; Feliu, Jaime; Pelaez-García, Alberto; Tang, Jing; Hardisson, David; Mendiola, Marta; Redondo, Andres.
Affiliation
  • Ramon-Patino JL; Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Ruz-Caracuel I; Department of Pathology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Heredia-Soto V; Translational Oncology Research Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain.
  • Garcia de la Calle LE; Center for Biomedical Research in the Cancer Network (Centro de Investigación Biomédica en Red de Cáncer, CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain.
  • Zagidullin B; Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Wang Y; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland.
  • Berjon A; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland.
  • Lopez-Janeiro A; Department of Pathology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Miguel M; Molecular Pathology and Therapeutic Targets Group, Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain.
  • Escudero J; Department of Pathology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Gallego A; Translational Oncology Research Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain.
  • Castelo B; Translational Oncology Research Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain.
  • Yebenes L; Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Hernandez A; Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Feliu J; Cátedra UAM-ANGEM, Faculty of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain.
  • Pelaez-García A; Department of Pathology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Tang J; Department of Obstetrics & Gynaecology, Hospital Universitario La Paz, IdiPAZ, 28046 Madrid, Spain.
  • Hardisson D; Faculty of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain.
  • Mendiola M; Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain.
  • Redondo A; Translational Oncology Research Laboratory, Hospital La Paz Institute for Health Research (IdiPAZ), 28046 Madrid, Spain.
Cancers (Basel) ; 14(4)2022 Feb 12.
Article in En | MEDLINE | ID: mdl-35205661
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Cancers (Basel) Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Cancers (Basel) Year: 2022 Document type: Article