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, 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 in questo prodotto:
File Dimensione Formato  
Clinical value of radiomics and machine learning in breast ultrasound a multicenter study for differential diagnosis of benign and malignant lesions.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 1.73 MB
Formato Adobe PDF
1.73 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/852162
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 18
  • ???jsp.display-item.citation.isi??? 17
social impact