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 ML.
Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions / Romeo, V., Cuocolo, R., Apolito, R., Stanzione, A., Ventimiglia, A., Vitale, A., Verde, F., Accurso, A., Amitrano, M., Insabato, L., Gencarelli, A., Buonocore, R., Argenzio, M.R., Cascone, A.M., Imbriaco, M., Maurea, S., Brunetti, A.. - In: EUROPEAN RADIOLOGY. - ISSN 0938-7994. - 31:12(2021), pp. 9511-9519. [10.1007/s00330-021-08009-2]
Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions
Romeo, Valeria;Cuocolo, Renato;Apolito, Roberta;Stanzione, Arnaldo
;Ventimiglia, Antonio;Vitale, Annalisa;Verde, Francesco;Accurso, Antonello;Insabato, Luigi;Gencarelli, Annarita;Buonocore, Roberta;Imbriaco, Massimo;Maurea, Simone;Brunetti, Arturo
2021
Abstract
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 ML.| File | Dimensione | Formato | |
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Clinical value of radiomics and machine learning in breast ultrasound a multicenter study for differential diagnosis of benign and malignant lesions.pdf
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