Machine and Deep Learning on Radiomic Features from Contrast-Enhanced Mammography and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Breast Cancer Characterization

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the predictio...

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Bibliographic Details

Published in:	Bioengineering
Main Authors:	Roberta Fusco, Vincenza Granata, Teresa Petrosino, Paolo Vallone, Maria Assunta Daniela Iasevoli, Mauro Mattace Raso, Sergio Venanzio Setola, Davide Pupo, Gerardo Ferrara, Annarita Fanizzi, Raffaella Massafra, Miria Lafranceschina, Daniele La Forgia, Laura Greco, Francesca Romana Ferranti, Valeria De Soccio, Antonello Vidiri, Francesca Botta, Valeria Dominelli, Enrico Cassano, Charlotte Marguerite Lucille Trombadori, Paolo Belli, Giovanna Trecate, Chiara Tenconi, Maria Carmen De Santis, Luca Boldrini, Antonella Petrillo
Format:	Article
Language:	English
Published:	MDPI AG 2025-09-01
Subjects:	radiomics DCE-MRI CEM machine learning deep learning breast cancer
Online Access:	https://www.mdpi.com/2306-5354/12/9/952