Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Roberta Fusco,Maria Rosaria Rubulotta,Paolo Vallone,Antonella Petrillo,Franca Avino,Giosuè Scognamiglio,Claudio Siani,Concetta Raiano,Maurizio Di Bonito,Mauro Mattace Raso,Vincenza Granata,Adele Piccirillo,Raimondo Di Giacomo,Elio Di Bernardo,Teresa Petrosino,Maria Luisa Barretta

doi:10.3390/curroncol29030159

Abstract

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Highlights

In the screening, detection and follow-up of breast cancer, the mammography (MX)was considered the first imaging examination [1,2]
dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important complementary diagnostic imaging technique that was validated in the screening of high-risk women and dense breasts and in the monitoring of oncological therapies, thanks to its capability of combining morphological and functional information [2,3]
Using texture features from dual-energy CEM and DCE-MRI, considered both individually and in combination, we aimed to evaluate radiomic analysis in discriminating between malignant and benign breast lesions

Summary

Introduction

Detection and follow-up of breast cancer, the mammography (MX)was considered the first imaging examination [1,2]. Thanks to the technological improvements achieved by combining digital mammography with techniques that allow low and high energy images to be obtained, and with the administration of iodate contrast agent, it is possible to acquire images that emphasize the vascularity linked to malignant lesions by the contrast agent enhancement. This imaging technique is recognized as contrast-enhanced mammography and exploits the same physiological mechanisms as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). CEM allows for the identification of additional occult cancers via mammography to more accurately assess the disease extent, and to guide surgical and treatment planning [8–12]

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