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Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions...

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Autores principales: Massafra, Raffaella, Bove, Samantha, Lorusso, Vito, Biafora, Albino, Comes, Maria Colomba, Didonna, Vittorio, Diotaiuti, Sergio, Fanizzi, Annarita, Nardone, Annalisa, Nolasco, Angelo, Ressa, Cosmo Maurizio, Tamborra, Pasquale, Terenzio, Antonella, La Forgia, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070152/
https://www.ncbi.nlm.nih.gov/pubmed/33920221
http://dx.doi.org/10.3390/diagnostics11040684
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author Massafra, Raffaella
Bove, Samantha
Lorusso, Vito
Biafora, Albino
Comes, Maria Colomba
Didonna, Vittorio
Diotaiuti, Sergio
Fanizzi, Annarita
Nardone, Annalisa
Nolasco, Angelo
Ressa, Cosmo Maurizio
Tamborra, Pasquale
Terenzio, Antonella
La Forgia, Daniele
author_facet Massafra, Raffaella
Bove, Samantha
Lorusso, Vito
Biafora, Albino
Comes, Maria Colomba
Didonna, Vittorio
Diotaiuti, Sergio
Fanizzi, Annarita
Nardone, Annalisa
Nolasco, Angelo
Ressa, Cosmo Maurizio
Tamborra, Pasquale
Terenzio, Antonella
La Forgia, Daniele
author_sort Massafra, Raffaella
collection PubMed
description Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.
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spelling pubmed-80701522021-04-26 Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images Massafra, Raffaella Bove, Samantha Lorusso, Vito Biafora, Albino Comes, Maria Colomba Didonna, Vittorio Diotaiuti, Sergio Fanizzi, Annarita Nardone, Annalisa Nolasco, Angelo Ressa, Cosmo Maurizio Tamborra, Pasquale Terenzio, Antonella La Forgia, Daniele Diagnostics (Basel) Article Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images. MDPI 2021-04-10 /pmc/articles/PMC8070152/ /pubmed/33920221 http://dx.doi.org/10.3390/diagnostics11040684 Text en © 2021 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
Massafra, Raffaella
Bove, Samantha
Lorusso, Vito
Biafora, Albino
Comes, Maria Colomba
Didonna, Vittorio
Diotaiuti, Sergio
Fanizzi, Annarita
Nardone, Annalisa
Nolasco, Angelo
Ressa, Cosmo Maurizio
Tamborra, Pasquale
Terenzio, Antonella
La Forgia, Daniele
Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title_full Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title_fullStr Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title_full_unstemmed Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title_short Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images
title_sort radiomic feature reduction approach to predict breast cancer by contrast-enhanced spectral mammography images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070152/
https://www.ncbi.nlm.nih.gov/pubmed/33920221
http://dx.doi.org/10.3390/diagnostics11040684
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