Cargando…
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...
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: | |
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 |
Ejemplares similares
-
Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study
por: Stanzione, Arnaldo, et al.
Publicado: (2022) -
Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges
por: Stanzione, Arnaldo, et al.
Publicado: (2022) -
MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer
por: Romeo, Valeria, et al.
Publicado: (2023) -
Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis
por: Ugga, Lorenzo, et al.
Publicado: (2021) -
Radiomics and machine learning applications in rectal cancer: Current update and future perspectives
por: Stanzione, Arnaldo, et al.
Publicado: (2021)