Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography

Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography

Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast canc...

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Bibliographic Details
Main Authors: Arezzo, F. (Author), Barretta, M.L (Author), Boldrini, L. (Author), Di Bernardo, E. (Author), Di Bonito, M. (Author), Fanizzi, A. (Author), Fusco, R. (Author), Granata, V. (Author), La Forgia, D. (Author), Massafra, R. (Author), Petrillo, A. (Author), Petrosino, T. (Author), Petruzzellis, N. (Author), Porto, A. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
artificial intelligence
Contrast-Enhanced Mammography (CEM)
Dynamic Contrast Magnetic Resonance Imaging (DCE-MRI)
radiomics
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