Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial
Year: 2018
Type: article
Abstract: Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio (SERmean) was calculated for all lesions, and quantitative pharmacokinetic, parameters Ktrans, kep, and ve, were calculated for the subset with available T1 maps (n=35). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for Ktrans, kep, and SERmean, respectively (p<0.05). At equal 94% sensitivity, the specificity and positive predictive value of SERmean (53% and 63%, respectively) and Ktrans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining SERmean and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using SERmean. SERmean has potential to help reduce unnecessary biopsies resulting from routine breast imaging.
Source: Journal of medical imaging
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy, Seattle Cancer Consortium Breast SPORE, Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy, MR Diffusion Tensor Imaging for Detection and Characterization of Breast Lesions, Commercialization of the Shutter-Speed Model for Dynamic MRI in Cancer Diagnosis, Recruitment of Established Investigators, Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer, G6870013078, G8141834623, OHSU Knight Cancer Institute
OpenAlex ID: W2785338963
