A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

Objective To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify lumi...

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Veröffentlicht in:	Radiologia medica 2024-06, Vol.129 (6), p.864-878
Hauptverfasser:	Petrillo, Antonella, Fusco, Roberta, Petrosino, Teresa, Vallone, Paolo, Granata, Vincenza, Rubulotta, Maria Rosaria, Pariante, Paolo, Raiano, Nicola, Scognamiglio, Giosuè, Fanizzi, Annarita, Massafra, Raffaella, Lafranceschina, Miria, La Forgia, Daniele, Greco, Laura, Ferranti, Francesca Romana, De Soccio, Valeria, Vidiri, Antonello, Botta, Francesca, Dominelli, Valeria, Cassano, Enrico, Sorgente, Eugenio, Pecori, Biagio, Cerciello, Vincenzo, Boldrini, Luca
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adult Aged Artificial Intelligence Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Breast Radiology Classification Contrast Media Cranium Decision trees Diagnostic Radiology Female Growth factors Humans Image contrast Image enhancement Imaging Interventional Radiology Italy Lesions Mammography Mammography - methods Medicine Medicine & Public Health Middle Aged Multivariate analysis Neoplasm Grading Neuroradiology Performance evaluation Radiographic Image Interpretation, Computer-Assisted - methods Radiology Radiomics Receptor, ErbB-2 Receptors Regression models Sensitivity and Specificity Ultrasound
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