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

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.

Contrast-Enhanced Spectral Mammography Assessment of Patients Treated with Neoadjuvant Chemotherapy for Breast Cancer

“…Radiomics in MRI can be more effective in the diagnosis of breast cancer and in the histological and morphological assessment of tumors. For CESM, a radiomics model achieved a significantly better discriminative ability compared to the standard clinical model (AUC, 0.81 vs. 0.55, p < 0.01) [43][44][45].…”

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

“…Objective quantification of reproducibility, stability and redundancy of features is a prerequisite for radiomics. This kind of process has been performed widely in radiomics [5][6][7][8][9][10][11][12], and it is even more meaningful when performed in a multicentric setting [13,14]. Dosiomics is born directly as an extension of radiomics; it entails extracting features from the patients' three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images [15,16] to obtain specific spatial and statistical information.…”

Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates

“…Another possible development of this study could be a comparison of the performance of CAD systems associated with mammography [ 45 , 46 , 47 , 48 , 49 ] and that obtained by the association between mammography and 3D ABUS.…”