A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up versus tissue sampling. From a retrospective 2015–2019 series of ultrasound-guided core needl...

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Detalles Bibliográficos
Autores principales: Interlenghi, Matteo, Salvatore, Christian, Magni, Veronica, Caldara, Gabriele, Schiavon, Elia, Cozzi, Andrea, Schiaffino, Simone, Carbonaro, Luca Alessandro, Castiglioni, Isabella, Sardanelli, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774734/
https://www.ncbi.nlm.nih.gov/pubmed/35054354
http://dx.doi.org/10.3390/diagnostics12010187

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774734/
https://www.ncbi.nlm.nih.gov/pubmed/35054354
http://dx.doi.org/10.3390/diagnostics12010187

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