Login / Signup

SENECA study: staging endometrial cancer based on molecular classification.

Enrique ChacónFelix Boria AlegreR Rajagopalan IyerFrancesco FanfaniMario MalzoniPetra BretováAna Luzarraga AznarRobert FruscioMarcin A JedrykaRichard TóthAnna Myriam PerroneAthanasios KakkosIgnacio CristobalLuigi CongedoVanna ZanagnoloSergi FernandezBeatriz FerroFabrice NarducciTatevik HovhannisyanElif AksahinLaura CardenasM Reyes OliverGonzalo NozaledaMarta ArnaezMarcin MisiekAnnamaria FerreroFlore Anne PainJanire ZarragoitiaCristina DiazLorenzo CeppiShamsi MehdiyevFernando Roldán-RivasAlberto Rafael Guijarro-CampilloJoana Amengual VilaNabil ManzourLuisa Sanchez LorenzoJorge M Núñez-CórdobaAntonio Gonzáles MartinJose Angel MinguezLuis Chiva de Agustínnull null
Published in: International journal of gynecological cancer : official journal of the International Gynecological Cancer Society (2024)
Our study reveals significant differences in SLN involvement among patients with early-stage endometrial cancer based on molecular subtypes. This underscores the importance of considering molecular characteristics for accurate staging and optimal management decisions.
Keyphrases
  • endometrial cancer
  • early stage
  • lymph node
  • machine learning
  • pet ct
  • squamous cell carcinoma
  • radiation therapy
  • sentinel lymph node
  • locally advanced