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 language | English |
|---|---|
| Article number | 5076269 |
| Journal | Contrast Media and Molecular Imaging |
| Volume | 2018 |
| DOIs | |
| Publication status | Published - Jan 1 2018 |
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ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
Cite this
DCE-MRI pharmacokinetic-based phenotyping of invasive ductal carcinoma : A radiomic study for prediction of histological outcomes. / Monti, Serena; Aiello, Marco; Incoronato, Mariarosaria; Grimaldi, Anna Maria; Moscarino, Michela; Mirabelli, Peppino; Ferbo, Umberto; Cavaliere, Carlo; Salvatore, Marco.
In: Contrast Media and Molecular Imaging, Vol. 2018, 5076269, 01.01.2018.Research output: Contribution to journal › Article
}
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 -