Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligenc...

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Bibliographic Details
Main Authors: Roberta Fusco, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria Luisa Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, Antonella Petrillo
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Applied Sciences
Subjects:
breast cancer
radiomics
artificial intelligence
classification
Online Access:https://www.mdpi.com/2076-3417/11/4/1880

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