Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer
Masatoyo Nakajo, Megumi Jinguji, Atsushi Tani, Hidehiko Kikuno, Daisuke Hirahara, Shinichi Togami, Hiroaki Kobayashi, Takashi Yoshiura
Topics & Concepts
Random forestProportional hazards modelArtificial intelligenceReceiver operating characteristicLogistic regressionMedicineEndometrial cancerHazard ratioPositron emission tomographyStage (stratigraphy)Naive Bayes classifierProgression-free survivalPET-CTSurvival analysisMachine learningNuclear medicineCancerOncologyComputer scienceSupport vector machineInternal medicineOverall survivalConfidence intervalPaleontologyBiologyRadiomics and Machine Learning in Medical ImagingEndometrial and Cervical Cancer TreatmentsMRI in cancer diagnosis