Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Study results show that our MR imaging computer-aided diagnosis algorithm, with use of a combination of computer-extracted MR imaging kinetic and morphologic features, has the potential to be extended to two prognostic tasks: (a) classification of noninvasive (ductal carcinoma in situ) versus invasive (invasive ductal carcinoma [IDC]) lesions and (b) further classification of IDC lesions into lesions with positive lymph nodes (LNs) and lesions with negative LNs

Ductal Carcinoma in Situ: X-ray Fluorescence Microscopy and Dynamic Contrast-enhanced MR Imaging Reveals Gadolinium Uptake within Neoplastic Mammary Ducts in a Murine Model1

We used a transgenic mouse model of breast cancer to investigate contrast enhancement of ductal carcinoma in situ (DCIS) on clinical dynamic contrast material–enhanced (DCE) MR images of the breast, and we have shown via two independent routes—DCE MR imaging and x-ray fluorescence microscopy—that after injection of gadodiamide, there is gadolinium uptake inside ducts distended with murine DCIS

DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement?

To perform a pilot study investigating whether the sensitivity and specificity of kinetic parameters can be improved by considering mass and nonmass breast lesions separately. The contrast media uptake and washout kinetics in benign and malignant breast lesions were analyzed using an empirical mathematical model (EMM), and model parameters were compared in lesions with mass-like and nonmass-like enhancement characteristics. 34 benign and 78 malignant breast lesions were selected for review. Dynamic MR protocol: 1 pre and 5 postcontrast images acquired in the coronal plane using a 3D T1-weighted SPGR with 68 s timing resolution. An experienced radiologist classified the type of enhancement as mass, nonmass, or focus, according to the BI-RADS® lexicon. The kinetic curve obtained from a radiologist-drawn region within the lesion was analyzed quantitatively using a three parameter EMM. Several kinetic parameters were then derived from the EMM parameters: the initial slope (Slopeini), curvature at the peak (κpeak), time to peak (Tpeak), initial area under the curve at 30 s (iAUC30), and the signal enhancement ratio (SER). The BI-RADS classification of the lesions yielded: 70 mass lesions, 38 nonmass, 4 focus. For mass lesions, the contrast uptake rate (α), contrast washout rate (β), iAUC30, SER, Slopeini, Tpeak and κpeak differed substantially between benign and malignant lesions, and after correcting for multiple tests of significance SER and Tpeak demonstrated significance (p<0.007). For nonmass lesions, we did not find statistically significant differences in any of the parameters for benign vs. malignant lesions (p>0.5). Kinetic parameters could distinguish benign and malignant mass lesions effectively, but were not quite as useful in discriminating benign from malignant nonmass lesions. If the results of this pilot study are validated in a larger trial, we expect that to maximize diagnostic utility, it will be better to classify lesion morphology as mass or nonmass-like enhancement prior to kinetic analysis