Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breas...

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Bibliographic Details
Main Authors: Roberta Fusco, Adele Piccirillo, Mario Sansone, Vincenza Granata, Maria Rosaria Rubulotta, Teresa Petrosino, Maria Luisa Barretta, Paolo Vallone, Raimondo Di Giacomo, Emanuela Esposito, Maurizio Di Bonito, Antonella Petrillo
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Diagnostics
Subjects:
breast
contrast-enhanced digital mammography
radiomics
artificial intelligence
Online Access:https://www.mdpi.com/2075-4418/11/5/815

Internet

https://www.mdpi.com/2075-4418/11/5/815

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