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Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Jun 28, 2020; 1(1): 6-18
Published online Jun 28, 2020. doi: 10.35711/aimi.v1.i1.6
Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art
Alessia Orlando, Mariangela Dimarco, Roberto Cannella, Tommaso Vincenzo Bartolotta
Alessia Orlando, Mariangela Dimarco, Roberto Cannella, Tommaso Vincenzo Bartolotta, Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
Tommaso Vincenzo Bartolotta, Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Palermo 90015, Italy
Author contributions: Orlando A and Dimarco M wrote and revised the manuscript for important intellectual content; Cannella R and Bartolotta TV made critical revisions related to important intellectual content of the manuscript; all the authors approved the final version of the article.
Conflict-of-interest statement: No conflict of interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Tommaso Vincenzo Bartolotta, MD, PhD, Associate Professor, Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Via Del Vespro 129, Palermo 90127, Italy. tommasovincenzo.bartolotta@unipa.it
Received: June 1, 2020
Peer-review started: June 1, 2020
First decision: June 5, 2020
Revised: June 17, 2020
Accepted: June 20, 2020
Article in press: June 20, 2020
Published online: June 28, 2020
Processing time: 38 Days and 14.2 Hours
Abstract

Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI.

Keywords: Radiomics; Texture analysis; Magnetic resonance imaging; Dynamic contrast-enhanced-magnetic resonance imaging; Breast; Cancer

Core tip: Dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) has been evaluated in most of radiomics studies on breast cancers. However, heterogeneity in study designs related to magnetic field, contrast media used, and software available to perform radiomics challenge the comparisons of available results. In this review we will focus on the following applications of radiomics in breast DCE-MRI: characterization of breast lesions, prediction of breast cancer histological types, correlation with receptor status, prediction of lymph node metastases, prediction of tumor response to neoadjuvant systemic therapy, prognosis and recurrence risks.