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A novel algorithm to implement the molecular classification according to the new ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer.

Ilaria BetellaCaterina FumagalliPaola Rafaniello RavieleGabriella SchivardiLuigi Antonio De VitisMaria Teresa AchilarreAlessia AloisiAnnalisa GarbiMatteo MaruccioVanna ZanagnoloGiovanni AlettiElena Guerini-RoccoAndrea MarianiAngelo MaggioniMassimo BarberisNicoletta ColomboFrancesco Multinu
Published in: International journal of gynecological cancer : official journal of the International Gynecological Cancer Society (2022)
Molecular categorization of endometrial cancer allows the reallocation of a considerable proportion of patients in a different risk class. Furthermore, the application of our algorithm enables a reduction in the number of required tests without affecting the risk classification.
Keyphrases
  • endometrial cancer
  • machine learning
  • deep learning
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • single molecule
  • clinical practice