Conte, L.; Rizzo, R.; Sallustio, A.; Maggiulli, E.; Capodieci, M.; Tramacere, F.; Castelluccia, A.; Raso, G.; De Giorgi, U.; Massafra, R.;
et al. Radiomics and Machine Learning Approaches for the Preoperative Classification of In Situ vs. Invasive Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE–MRI). Appl. Sci. 2025, 15, 7999.
https://doi.org/10.3390/app15147999
AMA Style
Conte L, Rizzo R, Sallustio A, Maggiulli E, Capodieci M, Tramacere F, Castelluccia A, Raso G, De Giorgi U, Massafra R,
et al. Radiomics and Machine Learning Approaches for the Preoperative Classification of In Situ vs. Invasive Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE–MRI). Applied Sciences. 2025; 15(14):7999.
https://doi.org/10.3390/app15147999
Chicago/Turabian Style
Conte, Luana, Rocco Rizzo, Alessandra Sallustio, Eleonora Maggiulli, Mariangela Capodieci, Francesco Tramacere, Alessandra Castelluccia, Giuseppe Raso, Ugo De Giorgi, Raffaella Massafra,
and et al. 2025. "Radiomics and Machine Learning Approaches for the Preoperative Classification of In Situ vs. Invasive Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE–MRI)" Applied Sciences 15, no. 14: 7999.
https://doi.org/10.3390/app15147999
APA Style
Conte, L., Rizzo, R., Sallustio, A., Maggiulli, E., Capodieci, M., Tramacere, F., Castelluccia, A., Raso, G., De Giorgi, U., Massafra, R., Portaluri, M., Cascio, D., & De Nunzio, G.
(2025). Radiomics and Machine Learning Approaches for the Preoperative Classification of In Situ vs. Invasive Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE–MRI). Applied Sciences, 15(14), 7999.
https://doi.org/10.3390/app15147999
