The general aim of this project is to improve dynamic contrast enhanced (DCE) MRI capability for benign and malignant breast disease discrimination, using a new analytical pharmacokinetic approach - the Shutter- Speed Model (SSM). Breast cancer is the second leading cause of cancer death in women. Though MRI has higher sensitivity than mammography and ultrasound for breast cancer detection, all three imaging modalities have limited specificities. This leads to possibly unnecessary (benign) biopsies, as well as undesirable complications and patient care consequences. There are three basic approaches for analyzing DCE MRI signal time-courses: qualitative subjective assessment, empirical quantitation, and analytical modeling. The last is most desirable, since the pharmacokinetic parameters extracted should be independent of data acquisition details, contrast reagent (CR) dose and injection rate, personal skill, etc. The conventional analysis - the Standard Model (SM) - ignores equilibrium transcytolemmal water exchange effects during CR bolus passage through the lesion, which can lead to significant underestimation, and CR dose- and injection rate-dependence, of the pharmacokinetic parameters, and thus low specificity. Preliminary SSM analyses (accounting for water exchange effects) of DCE MRI data from 22 patients eliminate the CR administration dependencies, and show perfect sensitivity and specificity.
The specific aims are: 1.) Evaluate the hypothesis that SSM analyses of DCE MRI data will significantly improve benign and malignant breast disease discrimination (compared to the SM approach) in a statistically significant population. 2.) Determine the SSM pharmacokinetic parameter thresholds that separate benign and malignant breast lesions with highest positive predictive value without sacrificing sensitivity. 3.) Evaluate the effects of varying temporal resolution and arterial input function determination on 1.) and 2.). T1-weighted gradient echo DCE MRI data from patients with clinically suspicious lesions will be collected during their scheduled MRI-guided preoperative needle localization or biopsy procedures, and will be analyzed with both SM and SSM. The pharmacokinetic parameters will be correlated with pathology results for statistical analyses. If successful, this SSM method may potentially be incorporated into clinical breast MRI protocols to reduce possibly unnecessary (benign) biopsies, and may be valuable in monitoring cancer treatment.
