Buzatto, Isabela Panzeri Carlotti 
; Recife, S. A.; Miguel, Leandro Fleck Fadel ; Bonini, R. M.; Onari, N.; Faim, A. L. P. A.; Silvestre, L.; Carlotti, Danilo Panzeri ; Fröhlich, Antônio Augusto ; Tiezzi, Daniel Guimarães
Source: Breast Cancer Research and Treatment. Unidade: FMRP
Subjects: NEOPLASIAS MAMÁRIAS, APRENDIZADO COMPUTACIONAL, MEDICINA PREVENTIVA, BIOMARCADORES, ESTATÍSTICA
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BUZATTO, Isabela Panzeri Carlotti et al. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features. Breast Cancer Research and Treatment, v. 211, p. 581-593, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10549-024-07429-0. Acesso em: 25 abr. 2026.APA
Buzatto, I. P. C., Recife, S. A., Miguel, L. F. F., Bonini, R. M., Onari, N., Faim, A. L. P. A., et al. (2024). Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features. Breast Cancer Research and Treatment, 211, 581-593. doi:10.1007/s10549-024-07429-0NLM
Buzatto IPC, Recife SA, Miguel LFF, Bonini RM, Onari N, Faim ALPA, Silvestre L, Carlotti DP, Fröhlich AA, Tiezzi DG. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features [Internet]. Breast Cancer Research and Treatment. 2024 ; 211 581-593.[citado 2026 abr. 25 ] Available from: https://doi.org/10.1007/s10549-024-07429-0Vancouver
Buzatto IPC, Recife SA, Miguel LFF, Bonini RM, Onari N, Faim ALPA, Silvestre L, Carlotti DP, Fröhlich AA, Tiezzi DG. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features [Internet]. Breast Cancer Research and Treatment. 2024 ; 211 581-593.[citado 2026 abr. 25 ] Available from: https://doi.org/10.1007/s10549-024-07429-0
