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

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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Bibliographic Details
Main Authors: Matteo Interlenghi, Christian Salvatore, Veronica Magni, Gabriele Caldara, Elia Schiavon, Andrea Cozzi, Simone Schiaffino, Luca Alessandro Carbonaro, Isabella Castiglioni, Francesco Sardanelli
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Diagnostics
Subjects:
breast cancer
ultrasound (US)
core needle biopsy
machine learning
radiomics
sensitivity
Online Access:https://www.mdpi.com/2075-4418/12/1/187

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