Role of Machine Learning (ML)-Based Classification Using Conventional 18 F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness.
Carolina BezziAlice BergaminiGregory MathouxSamuele GhezzoLavinia MonacoGiorgio CandottiFederico FallancaAna Maria Samanes GajateEmanuela RabaiottiRaffaella CioffiLuca BoccioloneLuigi GianolliGianLuca TaccagniMassimo CandianiGiorgia MangiliPaola MapelliMaria PicchioPublished in: Cancers (2023)
F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.
Keyphrases
- pet ct
- endometrial cancer
- machine learning
- positron emission tomography
- pet imaging
- end stage renal disease
- computed tomography
- ejection fraction
- newly diagnosed
- chronic kidney disease
- big data
- deep learning
- prognostic factors
- peritoneal dialysis
- patients undergoing
- mass spectrometry
- atomic force microscopy
- patient reported
- data analysis