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Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions
Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Me...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947713/ https://www.ncbi.nlm.nih.gov/pubmed/35323359 http://dx.doi.org/10.3390/curroncol29030159 |
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author | Fusco, Roberta Di Bernardo, Elio Piccirillo, Adele Rubulotta, Maria Rosaria Petrosino, Teresa Barretta, Maria Luisa Mattace Raso, Mauro Vallone, Paolo Raiano, Concetta Di Giacomo, Raimondo Siani, Claudio Avino, Franca Scognamiglio, Giosuè Di Bonito, Maurizio Granata, Vincenza Petrillo, Antonella |
author_facet | Fusco, Roberta Di Bernardo, Elio Piccirillo, Adele Rubulotta, Maria Rosaria Petrosino, Teresa Barretta, Maria Luisa Mattace Raso, Mauro Vallone, Paolo Raiano, Concetta Di Giacomo, Raimondo Siani, Claudio Avino, Franca Scognamiglio, Giosuè Di Bonito, Maurizio Granata, Vincenza Petrillo, Antonella |
author_sort | Fusco, Roberta |
collection | PubMed |
description | Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions. |
format | Online Article Text |
id | pubmed-8947713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89477132022-03-25 Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions Fusco, Roberta Di Bernardo, Elio Piccirillo, Adele Rubulotta, Maria Rosaria Petrosino, Teresa Barretta, Maria Luisa Mattace Raso, Mauro Vallone, Paolo Raiano, Concetta Di Giacomo, Raimondo Siani, Claudio Avino, Franca Scognamiglio, Giosuè Di Bonito, Maurizio Granata, Vincenza Petrillo, Antonella Curr Oncol Article Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions. MDPI 2022-03-13 /pmc/articles/PMC8947713/ /pubmed/35323359 http://dx.doi.org/10.3390/curroncol29030159 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fusco, Roberta Di Bernardo, Elio Piccirillo, Adele Rubulotta, Maria Rosaria Petrosino, Teresa Barretta, Maria Luisa Mattace Raso, Mauro Vallone, Paolo Raiano, Concetta Di Giacomo, Raimondo Siani, Claudio Avino, Franca Scognamiglio, Giosuè Di Bonito, Maurizio Granata, Vincenza Petrillo, Antonella Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title | Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title_full | Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title_fullStr | Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title_full_unstemmed | Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title_short | Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions |
title_sort | radiomic and artificial intelligence analysis with textural metrics extracted by contrast-enhanced mammography and dynamic contrast magnetic resonance imaging to detect breast malignant lesions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947713/ https://www.ncbi.nlm.nih.gov/pubmed/35323359 http://dx.doi.org/10.3390/curroncol29030159 |
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