Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Autor: Buzatto IPC; Department of Obstetrics and Gynecology - Breast Disease Division, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil., Recife SA; Department of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil., Miguel L; Department of Gynecology & Obstetrics, Women's Health Reference Center of Ribeirão Preto (MATER), Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil., Bonini RM; Department of Radiology, Hospital de Amor de Campo Grande, Campo Grande, Mato Grosso Do Sul, Brazil., Onari N; Department of Radiology, Hospital de Amor de Barretos, Barretos, Brazil., Faim ALPA; Department of Radiology, Hospital de Amor de Barretos, Barretos, Brazil., Silvestre L; Department of Obstetrics and Gynecology - Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil., Carlotti DP; Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil., Fröhlich A; Department of Mathematics, Federal University of Santa Catarina, Florianópolis, Brazil., Tiezzi DG; Department of Obstetrics and Gynecology - Breast Disease Division and Laboratory for Translational Data Science, Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3.900, Monte Alegre, Ribeirão Preto, Ribeirão Preto, Brazil. dtiezzi@usp.br.; Advanced Research Center in Medicine, Union of the Colleges of the Great Lakes (UNILAGO), São José Do Rio Preto, São Paulo, Brazil. dtiezzi@usp.br.

Publikováno v: Breast cancer research and treatment [Breast Cancer Res Treat] 2024 Jul 13. Date of Electronic Publication: 2024 Jul 13.