Purpose:To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions. Materials and Methods:In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were fi rst selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classifi cation performance was assessed with receiver operating characteristic analysis. Results: Conclusion:Computer-aided diagnosis of breast DCE MR imagingdepicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/ suppl

http://radiology.rsnajnls.org/cgi/content/full/245/3/684/DC1.

Purpose:To combine dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging with x-ray fluorescence microscopy (XFM) of mammary gland tissue samples from mice to identify the spatial distribution of gadolinium after intravenous injection. Materials and Methods:C3(1) Sv-40 large T antigen transgenic mice (n ϭ 23) were studied with institutional animal care and use committee approval. Twelve mice underwent DCE MR imaging after injection of gadodiamide, and gadolinium concentrationtime curves were fit to a two-compartment pharmacokinetic model with the following parameters: transfer constant (K trans ) and volume of extravascular extracellular space per unit volume of tissue (v e ). Eleven mice received gadodiamide before XFM. These mice were sacrificed 2 minutes after injection, and frozen slices containing ducts distended with murine ductal carcinoma in situ (DCIS) were prepared for XFM. One mouse received saline and served as the control animal. Elemental gadolinium concentrations were measured in and around the ducts with DCIS. Hematoxylin-eosin-stained slices of mammary tissues were obtained after DCE MR imaging and XFM. Results:Ducts containing DCIS were unambiguously identified on MR images. DCE MR imaging revealed gadolinium uptake along the length of ducts with DCIS, with an average K trans of 0.21 min Ϫ1 Ϯ 0.14 (standard deviation) and an average v e of 0.40 Ϯ 0.16. XFM revealed gadolinium uptake inside ducts with DCIS, with an average concentration of 0.475 mmol/L Ϯ 0.380; the corresponding value for DCE MR imaging was 0.30 mmol/L Ϯ 0.13. Conclusion:These results provide insight into the physiologic basis of contrast enhancement of DCIS lesions on DCE MR images: Gadolinium penetrates and collects inside neoplastic ducts. RSNA, 2009 Note: This copy is for your personal, non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, use the Radiology Reprints form at the end of this article.T he sensitivity and specificity of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging in the early detection of invasive cancers have been shown to be equal or superior to those of x-ray mammography (1). However, this has not been consistently demonstrated for ductal carcinoma in situ (DCIS), which is a nonobligate precursor to invasive breast cancer, in which cancer cells are still confined by the basement membrane of mammary ducts. Because DCIS is the earliest stage of breast cancer and has the best prognosis, it is likely that further improvements in the detection of breast cancers at a preinvasive stage may improve patient outcomes. Some reports have shown decreased diagnostic accuracy of DCE MR imaging for DCIS (2,3), while others have shown comparable or even higher performance compared with that of x-ray mammography (4,5). The sensitivity of DCE MR imaging for detection of DCIS may be compromised if the lesion does not exhibit sufficient gadolinium uptake or if it is obscured by strongly enhancing parenchyma (6,7). Even when D...

The purpose of this study was to apply an empirical mathematical model (EMM) to kinetic data acquired under a clinical protocol to determine if the sensitivity and specificity can be improved compared with qualitative BI-RADS descriptors of kinetics. 3D DCE-MRI data from 100 patients with 34 benign and 79 malignant lesions were selected for review under an Institutional Review Board (IRB)-approved protocol. The sensitivity and specificity of the delayed phase classification were 91% and 18%, respectively. The EMM was able to accurately fit these curves. There was a statistically significant difference between benign and malignant lesions for several model parameters: the uptake rate, initial slope, signal enhancement ratio, and curvature at the peak enhancement (at most P ‫؍‬ 0.04). These results demonstrated that EMM analysis provided at least the diagnostic accuracy of the kinetic classifiers described in the BI-RADS lexicon, and offered a few key advantages. It can be used to standardize data from institutions with different dynamic protocols and can provide a more objective classification with continuous variables so that thresholds can be set to achieve desired sensitivity and specificity. Key words: malignant; breast; DCE-MRI; sensitivityImprovements in breast cancer detection are largely responsible for increasing survival among breast cancer patients (1). Dynamic contrast-enhanced MRI (DCE-MRI) is being used in breast imaging for several purposes, including determining extent of malignant disease and posttreatment evaluation (2,3). DCE-MRI has a high sensitivity to breast cancer, with a lower specificity (4 -6). When analyzing DCE-MRI the radiologist assesses both the lesion morphology and kinetics of contrast enhancement. Some studies have suggested that the morphologic information from DCE-MRI is more diagnostically useful than the kinetic information (7,8), implying that there may be room for improvement in extracting more diagnostically relevant information from kinetic data.Ideally, DCE-MRI protocols would acquire data with high spatial and high temporal resolution to fully exploit both the morphologic and kinetic information. Unfortunately, with currently available equipment and techniques there is always a trade-off between spatial and temporal resolution in DCE-MRI (7). As a result, the signal intensity versus time-or kinetic-curves typically have only 3-7 data points (9 -11) for 3D DCE-MRI, which presents a challenge for differentiating benign from malignant lesions. To simplify analysis of the kinetic curves, radiologists qualitatively assess the initial rise and delayed phase according to the BI-RADS lexicon. Several reports have demonstrated that DCE-MRI data from malignant lesions tend to exhibit "washout" curves, while benign lesions tend to show persistent signal increase with time after contrast injection (12,13). Some groups have performed semiquantitative analysis of these curves-for example, calculating the time to peak enhancement-to better distinguish between the benign and malignan...

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 ͑EMM͒, and model parameters were compared in lesions with masslike 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 ͑Slope ini ͒, curvature at the peak ͑ peak ͒, time to peak ͑T peak ͒, initial area under the curve at 30 s ͑iAUC 30 ͒, and the signal enhancement ratio ͑SER͒. The BI-RADS classification of the lesions yielded: 70 mass lesions, 38 nonmass, 4 focus. For mass lesions, the contrast uptake rate ͑␣͒, contrast washout rate ͑␤͒, iAUC 30 , SER, Slope ini , T peak and peak differed substantially between benign and malignant lesions, and after correcting for multiple tests of significance SER and T peak demonstrated significance ͑p Ͻ 0.007͒. For nonmass lesions, we did not find statistically significant differences in any of the parameters for benign vs. malignant lesions ͑p Ͼ 0.5͒. 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.