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

Authors :
Raffaella Massafra
Samantha Bove
Vito Lorusso
Albino Biafora
Maria Colomba Comes
Vittorio Didonna
Sergio Diotaiuti
Annarita Fanizzi
Annalisa Nardone
Angelo Nolasco
Cosmo Maurizio Ressa
Pasquale Tamborra
Antonella Terenzio
Daniele La Forgia
Source :
Diagnostics, Vol 11, Iss 4, p 684 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20754418
Volume :
11
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.ba248ff936594aaba88024c7d1f84592
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics11040684