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Contrast Media & Molecular Imaging
Volume 2018, Article ID 5076269, 11 pages
Research Article

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

1IRCCS SDN, Naples, Italy
2Department of Pathology, Ospedale Moscati, Avellino, Italy

Correspondence should be addressed to Marco Aiello; ti.ilopan-nds@olleiam

Received 28 July 2017; Revised 20 November 2017; Accepted 18 December 2017; Published 17 January 2018

Academic Editor: Isabella Castiglioni

Copyright © 2018 Serena Monti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Breast cancer is a disease affecting an increasing number of women worldwide. Several efforts have been made in the last years to identify imaging biomarker and to develop noninvasive diagnostic tools for breast tumor characterization and monitoring, which could help in patients’ stratification, outcome prediction, and treatment personalization. In particular, radiomic approaches have paved the way to the study of the cancer imaging phenotypes. In this work, a group of 49 patients with diagnosis of invasive ductal carcinoma was studied. The purpose of this study was to select radiomic features extracted from a DCE-MRI pharmacokinetic protocol, including quantitative maps of , , , iAUC, and and to construct predictive models for the discrimination of molecular receptor status (ER+/ER, PR+/PR, and HER2+/HER2), triple negative (TN)/non-triple negative (NTN), ki67 levels, and tumor grade. A total of 163 features were obtained and, after feature set reduction step, followed by feature selection and prediction performance estimations, the predictive model coefficients were computed for each classification task. The AUC values obtained were for ER+/ER, for PR+/PR, for HER2+/HER2, for TN/NTN, for ki67+/ki67, and for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokinetic-based phenotyping shows promising for discrimination of the histological outcomes.