Eleonora Massari
Machine learningHIPO modelArtificial intelligenceRadiation treatment planningFecal incontinenceEndometrial cancerBrachytherapyVaginal cancerProstate cancerVaginal mucosaNuclear medicineRisk classificationDose distributionClassification methodsReady to useHigh dosesOptimization methodsArtificial neural networkRadiation therapyMedicine
Publications 2
#1Mauro CarraraH-Index: 16
#2Eleonora MassariH-Index: 2
Last. Riccardo Valdagni (University of Milan)H-Index: 51
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Purpose This study was designed to apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) after high-dose prostate cancer radiation therapy and to develop a ready-to-use graphical tool. Materials and Methods In this study, 598 men recruited in 2 national multicenter trials were analyzed. Information was recorded on comorbidity, previous abdominal surgery, use of drugs, and dose distribution. Fecal incontinence was prospectively evaluated ...
4 CitationsSource
#1Mauro CarraraH-Index: 16
#2Davide CusumanoH-Index: 11
Last. Emanuele PignoliH-Index: 19
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Abstract Purpose A direct planning approach with multi-channel vaginal cylinders (MVCs) used for HDR brachytherapy of vaginal cancers is particularly challenging. Purpose of this study was to compare the dosimetric performances of different forward and inverse methods used for the optimization of MVC-based vaginal treatments for endometrial cancer, with a particular attention to the definition of strategies useful to limit the high doses to the vaginal mucosa. Methods Twelve postoperative vagina...
5 CitationsSource