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Open AccessArticle

Radiomics Analysis on Contrast-Enhanced Spectral Mammography Images for Breast Cancer Diagnosis: A Pilot Study

1
Istituto Tumori “Giovanni Paolo II” I.R.C.C.S., 70124 Bari, Italy
2
Dip. Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari “A. Moro”, 70125 Bari, Italy
3
Dip. di Scienze Fisiche, della Terra e dell’Ambiente, Università degli Studi di Siena, 53100 Siena, Italy
4
Dip. di Scienza della Salute, Università degli Studi di Genova, 16132 Genova, Italy
5
INFN—Istituto Nazionale di Fisica Nucleare, 70126 Bari, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2019, 21(11), 1110; https://doi.org/10.3390/e21111110
Received: 8 October 2019 / Revised: 6 November 2019 / Accepted: 11 November 2019 / Published: 13 November 2019
(This article belongs to the Special Issue Statistical Inference from High Dimensional Data)
Contrast-enhanced spectral mammography is one of the latest diagnostic tool for breast care; therefore, the literature is poor in radiomics image analysis useful to drive the development of automatic diagnostic support systems. In this work, we propose a preliminary exploratory analysis to evaluate the impact of different sets of textural features in the discrimination of benign and malignant breast lesions. The analysis is performed on 55 ROIs extracted from 51 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. We extracted feature sets by calculating statistical measures on original ROIs, gradiented images, Haar decompositions of the same original ROIs, and on gray-level co-occurrence matrices of the each sub-ROI obtained by Haar transform. First, we evaluated the overall impact of each feature set on the diagnosis through a principal component analysis by training a support vector machine classifier. Then, in order to identify a sub-set for each set of features with higher diagnostic power, we developed a feature importance analysis by means of wrapper and embedded methods. Finally, we trained an SVM classifier on each sub-set of previously selected features to compare their classification performances with respect to those of the overall set. We found a sub-set of significant features extracted from the original ROIs with a diagnostic accuracy greater than 80 % . The features extracted from each sub-ROI decomposed by two levels of Haar transform were predictive only when they were all used without any selection, reaching the best mean accuracy of about 80 % . Moreover, most of the significant features calculated by HAAR decompositions and their GLCMs were extracted from recombined CESM images. Our pilot study suggested that textural features could provide complementary information about the characterization of breast lesions. In particular, we found a sub-set of significant features extracted from the original ROIs, gradiented ROI images, and GLCMs calculated from each sub-ROI previously decomposed by the Haar transform. View Full-Text
Keywords: breast cancer; radiomics analysis; feature extraction; feature selection; Haar wavelet decomposition; gray-level co-occurrence matrix; contrast-enhanced spectral mammography breast cancer; radiomics analysis; feature extraction; feature selection; Haar wavelet decomposition; gray-level co-occurrence matrix; contrast-enhanced spectral mammography
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Losurdo, L.; Fanizzi, A.; Basile, T.M.A.; Bellotti, R.; Bottigli, U.; Dentamaro, R.; Didonna, V.; Lorusso, V.; Massafra, R.; Tamborra, P.; Tagliafico, A.; Tangaro, S.; La Forgia, D. Radiomics Analysis on Contrast-Enhanced Spectral Mammography Images for Breast Cancer Diagnosis: A Pilot Study. Entropy 2019, 21, 1110.

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