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.
Highlights
We have developed an accurate computer-aided diagnosis tool to classify breast cancer lesions based on radiomic analysis of contrast-enhanced spectral mammography (CESM) images
We proposed a preliminary radiomics analysis aimed to explore the usefulness of quantitative information extracted from CESM images, to understand the behavior of each different set of well-known textural features automatically extracted from CESM images, and to compare them with each other [21,23]
We performed a principal component analysis (PCA) for each standardized set of features, we adopted the explained variance criterion to select a sub-set of discriminant principal components
Summary
In MRI the performance of the human reader can be reduced by the presence of marked background parenchymal enhancement (BPE), defined as the normal background impregnation of the breast but with very different intensity and distribution in relation to age, hormonal phase, menopausal state and any therapies in progress [6]. In these circumstances, diagnostic aids to reporting and fusion-imaging systems in different positions can support the diagnosis [7,8,9].
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