DCE-MRI pharmacokinetic-based phenotyping of invasive ductal carcinoma: A radiomic study for prediction of histological outcomes

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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 ktrans, kep, ve, iAUC, and R1 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 0.826 ± 0.006 for ER+/ER., 0.875 ± 0.009 for PR+/PR., 0.838 ± 0.006 for HER2+/HER2., 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67., and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokineticbased phenotyping shows promising for discrimination of the histological outcomes.

Original languageEnglish
Article number5076269
JournalContrast Media and Molecular Imaging
Volume2018
DOIs
Publication statusPublished - Jan 1 2018

Fingerprint

Ductal Carcinoma
Pharmacokinetics
Breast Neoplasms
Molecular Models
Area Under Curve
Neoplasms
Biomarkers
Phenotype

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

@article{ed2be14c4d744c6da29ebef4a40b34f7,
title = "DCE-MRI pharmacokinetic-based phenotyping of invasive ductal carcinoma: A radiomic study for prediction of histological outcomes",
abstract = "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 ktrans, kep, ve, iAUC, and R1 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 0.826 ± 0.006 for ER+/ER., 0.875 ± 0.009 for PR+/PR., 0.838 ± 0.006 for HER2+/HER2., 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67., and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokineticbased phenotyping shows promising for discrimination of the histological outcomes.",
author = "Serena Monti and Marco Aiello and Mariarosaria Incoronato and Grimaldi, {Anna Maria} and Michela Moscarino and Peppino Mirabelli and Umberto Ferbo and Carlo Cavaliere and Marco Salvatore",
year = "2018",
month = "1",
day = "1",
doi = "10.1155/2018/5076269",
language = "English",
volume = "2018",
journal = "Contrast Media and Molecular Imaging",
issn = "1555-4309",
publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - DCE-MRI pharmacokinetic-based phenotyping of invasive ductal carcinoma

T2 - A radiomic study for prediction of histological outcomes

AU - Monti, Serena

AU - Aiello, Marco

AU - Incoronato, Mariarosaria

AU - Grimaldi, Anna Maria

AU - Moscarino, Michela

AU - Mirabelli, Peppino

AU - Ferbo, Umberto

AU - Cavaliere, Carlo

AU - Salvatore, Marco

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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 ktrans, kep, ve, iAUC, and R1 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 0.826 ± 0.006 for ER+/ER., 0.875 ± 0.009 for PR+/PR., 0.838 ± 0.006 for HER2+/HER2., 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67., and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokineticbased phenotyping shows promising for discrimination of the histological outcomes.

AB - 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 ktrans, kep, ve, iAUC, and R1 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 0.826 ± 0.006 for ER+/ER., 0.875 ± 0.009 for PR+/PR., 0.838 ± 0.006 for HER2+/HER2., 0.876 ± 0.007 for TN/NTN, 0.811 ± 0.005 for ki67+/ki67., and 0.895 ± 0.006 for lowGrade/highGrade. In conclusion, DCE-MRI pharmacokineticbased phenotyping shows promising for discrimination of the histological outcomes.

UR - http://www.scopus.com/inward/record.url?scp=85044951560&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044951560&partnerID=8YFLogxK

U2 - 10.1155/2018/5076269

DO - 10.1155/2018/5076269

M3 - Article

AN - SCOPUS:85044951560

VL - 2018

JO - Contrast Media and Molecular Imaging

JF - Contrast Media and Molecular Imaging

SN - 1555-4309

M1 - 5076269

ER -