Journal
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
Volume 46, Issue 9, Pages 1878-1888Publisher
SPRINGER
DOI: 10.1007/s00259-019-04331-6
Keywords
Breast cancer; Positron emission tomography; Fluorodeoxyglucose; Magnetic resonance imaging; Imaging biomarker
Funding
- Medical University of Vienna
- Guerbet France
- National Institutes of Health/National Cancer Institute Cancer Center Support Grant [P30 CA008748]
- Breast Cancer Research Foundation
- Susan G. Komen Foundation
- Novomed Austria
- 2020 - Research and Innovation Framework Programme PHC-11-2015 [667211-2]
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PurposeTo develop a multiparametric [F-18]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue.MethodsIn this prospective study and retrospective data analysis, 141 patients (mean 57years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [F-18]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [F-18]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC).ResultsThere were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5-10cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P=0.0003), tumor ADCmean (P<0.001), and BPE of the contralateral healthy breast (P=0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis.ConclusionA multiparametric [F-18]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.
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