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For: Orlando A, Dimarco M, Cannella R, Bartolotta TV. Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art. Artif Intell Med Imaging 2020; 1(1): 6-18 [DOI: 10.35711/aimi.v1.i1.6]
URL: https://www.wjgnet.com/2218-4333/full/v1/i1/6.htm
Number Citing Articles
1
Francesco Ceccarelli, Francesco Prinzi, Pietro Liò, Salvatore Vitabile, Sean B. Holden. MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRI2024 International Joint Conference on Neural Networks (IJCNN) 2024; : 1 doi: 10.1109/IJCNN60899.2024.10650117
2
Carmelo Militello, Leonardo Rundo, Mariangela Dimarco, Alessia Orlando, Ramona Woitek, Ildebrando D'Angelo, Giorgio Russo, Tommaso Vincenzo Bartolotta. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer PatientsAcademic Radiology 2022; 29(6): 830 doi: 10.1016/j.acra.2021.08.024
3
Sachini Udara Wickramasinghe, Thushara Indika Weerakoon, Dr. Pradeep Jayantha Gamage, Dr. Muditha Suranga Bandara, Dr. Aruna Pallewatte. Identification of Radiomic Features as an Imaging Marker to Differentiate Benign and Malignant Breast Masses Based on Magnetic Resonance ImagingImaging 2022;  doi: 10.1556/1647.2022.00065
4
Francesco Prinzi, Alessia Orlando, Salvatore Gaglio, Massimo Midiri, Salvatore Vitabile. Applied Intelligence and InformaticsCommunications in Computer and Information Science 2022; 1724: 144 doi: 10.1007/978-3-031-24801-6_11
5
Francesco Prinzi, Alessia Orlando, Salvatore Gaglio, Salvatore Vitabile. Breast cancer classification through multivariate radiomic time series analysis in DCE-MRI sequencesExpert Systems with Applications 2024; 249: 123557 doi: 10.1016/j.eswa.2024.123557