Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions

OBJECTIVES: We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML’s accuracy with that of a breast radiologist, and verify if the radiologist’s performance is improved by using M...

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Detalles Bibliográficos
Autores principales: Romeo, Valeria, Cuocolo, Renato, Apolito, Roberta, Stanzione, Arnaldo, Ventimiglia, Antonio, Vitale, Annalisa, Verde, Francesco, Accurso, Antonello, Amitrano, Michele, Insabato, Luigi, Gencarelli, Annarita, Buonocore, Roberta, Argenzio, Maria Rosaria, Cascone, Anna Maria, Imbriaco, Massimo, Maurea, Simone, Brunetti, Arturo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Breast
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589755/
https://www.ncbi.nlm.nih.gov/pubmed/34018057
http://dx.doi.org/10.1007/s00330-021-08009-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589755/
https://www.ncbi.nlm.nih.gov/pubmed/34018057
http://dx.doi.org/10.1007/s00330-021-08009-2

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