Sanaz A. Jansen

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

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 first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis. RESULTS Differentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 +/- 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions. CONCLUSION Computer-aided diagnosis of breast DCE MR imaging-depicted 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. SUPPLEMENTAL MATERIAL http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09090838/-/DC1.

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

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 = 23) were studied with institutional animal care and use committee approval. Twelve mice underwent DCE MR imaging after injection of gadodiamide, and gadolinium concentration-time 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(-1) +/- 0.14 (standard deviation) and an average v(e) of 0.40 +/- 0.16. XFM revealed gadolinium uptake inside ducts with DCIS, with an average concentration of 0.475 mmol/L +/- 0.380; the corresponding value for DCE MR imaging was 0.30 mmol/L +/- 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.

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 68s 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 30s (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.

Kinetic Curves of Malignant Lesions Are Not Consistent Across MRI Systems: Need for Improved Standardization of Breast Dynamic Contrast-Enhanced MRI Acquisition

OBJECTIVE The purpose of this study was to compare MRI kinetic curve data acquired with three systems in the evaluation of malignant lesions of the breast. MATERIALS AND METHODS The cases of 601 patients with 682 breast lesions (185 benign, 497 malignant) were selected for review. The malignant lesions were classified as ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and other. The dynamic MRI protocol consisted of one unenhanced and three to seven contrast-enhanced images acquired with one of three imaging protocols and systems. An experienced radiologist analyzed the shapes of the kinetic curves according to the BI-RADS lexicon. Several quantitative kinetic parameters were calculated, and the kinetic parameters of malignant lesions were compared across the three systems. RESULTS Imaging protocol and system 1 were used to image 304 malignant lesions (185 IDC, 62 DCIS); imaging protocol and system 2, 107 lesions (72 IDC, 21 DCIS); and imaging protocol and system 3, 86 lesions (64 IDC, 17 DCIS). Compared with those visualized with imaging protocols and systems 1 and 2, IDC lesions visualized with imaging protocol and system 3 had significantly less initial enhancement, longer time to peak enhancement, and a slower washout rate (p < 0.004). Only 47% of IDC lesions imaged with imaging protocol and system 3 exhibited washout type curves, compared with 75% and 74% of those imaged with imaging protocols and systems 2 and 1, respectively. The diagnostic accuracy of kinetic analysis was lowest for imaging protocol and system 3, but the difference was not statistically significant. CONCLUSION The kinetic curve data on malignant lesions acquired with one system showed significantly lower initial contrast uptake and a different curve shape in comparison with data acquired with the other two systems. Differences in k-space sampling, T1 weighting, and magnetization transfer effects may be explanations for the difference.

Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: a sensitivity and specificity study.

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. This suggests that the EMM may be useful for analysis of routine clinical data. Magn Reson Med 59:747–754, 2008.

Quantitative Analysis of Dynamic Contrast Enhanced MRI for Assessment of Bowel Inflammation in Crohn's Disease: Pilot Study

RATIONALE AND OBJECTIVES The aim of this study was to evaluate the feasibility of quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data in the detection of bowel inflammation in patients with Crohns disease. MATERIALS AND METHODS Eleven patients who underwent magnetic resonance enterography for known or suspected Crohns disease and had colonoscopy or surgery within 4 weeks of imaging were included in this study. DCE-MRI data were acquired using a 1.5-T scanner with temporal resolution of 5 to 12 seconds for approximately 5 to 7 minutes. A two-compartment pharmacokinetic model was used to analyze the data to obtain the volume transfer constant (K(trans)) and the extravascular extracellular space (v(e)). Additionally, semiquantitative parameters were derived using an empirical mathematical model to fit the contrast concentration curves. Endoscopic, surgical, and pathologic results were compared to the MRI data. Receiver-operating characteristic analysis was performed to compare the diagnostic accuracy of the parameters in the task of distinguishing normal tissue from inflammation. RESULTS A total of 51 bowel segments (19 with inflammation, 32 normal) were included in the analyses. Inflamed bowel segments had faster K(trans) values, larger v(e) values, increased contrast uptake, larger initial areas under the contrast concentration curve, and steeper initial enhancement slopes than normal bowel segments (P < .05). The areas under the receiver-operating characteristic curve for these parameters ranged from 0.70 to 0.86. CONCLUSION These preliminary results demonstrate that the quantitative analysis of DCE-MRI data is possible for the assessment of bowel inflammation in patients with Crohns disease. Future studies need be performed on larger numbers of patients to correlate the severity and type of inflammation with kinetic parameters.

The diverse pathology and kinetics of mass, nonmass and focus enhancement on MR imaging of the breast

To compare the pathology and kinetic characteristics of breast lesions with focus‐, mass‐, and nonmass‐like enhancement.

Breast Cancers Not Detected at MRI: Review of False-Negative Lesions

OBJECTIVE The objective of our study was to determine the sensitivity of cancer detection at breast MRI using current imaging techniques and to evaluate the characteristics of lesions with false-negative examinations. MATERIALS AND METHODS Two hundred seventeen patients with 222 newly diagnosed breast cancers or highly suspicious breast lesions that were subsequently shown to be malignant underwent breast MRI examinations for staging. Two breast imaging radiologists performed a consensus review of the breast MRI examinations. The absence of perceptible contrast enhancement at the expected site was considered to be a false-negative MRI. Histology of all lesions was reviewed by an experienced breast pathologist. RESULTS Enhancement was observed in 213 (95.9%) of the 222 cancer lesions. Of the nine lesions without visible enhancement, two lesions were excluded because the entire tumor had been excised at percutaneous biopsy performed before the MRI examination and no residual tumor was noted on the final histology. The overall sensitivity of MRI for the known cancers was 96.8% (213/220); for invasive cancer, 98.3% (176/179); and for ductal carcinoma in situ, 90.2% (37/41). CONCLUSION In a population of 220 sequentially diagnosed breast cancer lesions, we found seven (3.2%) MRI-occult cancers, fewer than seen in other published studies. Small tumor size and diffuse parenchymal enhancement were the principal reasons for these false-negative results. Although the overall sensitivity of cancer detection was high (96.8%), it should be emphasized that a negative MRI should not influence the management of a lesion that appears to be of concern on physical examination or on other imaging techniques.

Evaluation of Computer-aided Diagnosis on a Large Clinical Full-field Digital Mammographic Dataset

RATIONALE AND OBJECTIVES To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer. MATERIALS AND METHODS An institutional review board approved protocol was obtained, with waiver of consent for retrospective use of mammograms and pathology data. Seven hundred thirty-nine FFDM images, which contained 287 biopsy-proven breast mass lesions, of which 148 lesions were malignant and 139 lesions were benign, were retrospectively collected. Lesion margins were delineated by an expert breast radiologist and were used as the truth for lesion-segmentation evaluation. Our computerized image analysis method consisted of several steps: 1) identified lesions were automatically extracted from the parenchymal background using computerized segmentation methods; 2) a set of image characteristics (mathematic descriptors) were automatically extracted from image data of the lesions and surrounding tissues; and 3) selected features were merged into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. Performance of the analyses was evaluated at various stages of the conversion using receiver-operating characteristic analysis. RESULTS An area under the curve value of 0.81 was obtained in the task of distinguishing between malignant and benign mass lesions in a round-robin by case evaluation on the entire FFDM dataset. We failed to show a statistically significant difference (P = .83) compared to results from our previous study in which the computerized classification was performed on digitized screen-film mammograms. CONCLUSIONS Our computerized analysis methods developed on digitized screen-film mammography can be converted for use with FFDM. Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.

Computerized Assessment of Breast Lesion Malignancy using DCE-MRI: Robustness Study on Two Independent Clinical Datasets from Two Manufacturers

RATIONALE AND OBJECTIVES To conduct a preclinical evaluation of the robustness of our computerized system for breast lesion characterization on two breast magnetic resonance imaging (MRI) databases that were acquired using scanners from two different manufacturers. MATERIALS AND METHODS Two clinical breast MRI databases were acquired from a Siemens scanner and a GE scanner, which shared similar imaging protocols and retrospectively collected under an institutional review board-approved protocol. In our computerized analysis system, after a breast lesion is identified by the radiologist, the computer performs automatic lesion segmentation and feature extraction and outputs an estimated probability of malignancy. We used a Bayesian neural network with automatic relevance determination for joint feature selection and classification. To evaluate the robustness of our classification system, we first used Database 1 for feature selection and classifier training, and Database 2 to test the trained classifier. Then, we exchanged the two datasets and repeated the process. Area under the receiver operating characteristic curve (AUC) was used as a performance figure of merit in the task of distinguishing between malignant and benign lesions. RESULTS We obtained an AUC of 0.85 (approximate 95% confidence interval [CI] 0.79-0.91) for (a) feature selection and classifier training using Database 1 and testing on Database 2; and an AUC of 0.90 (approximate 95% CI 0.84-0.96) for (b) feature selection and classifier training using Database 2 and testing on Database 1. We failed to observe statistical significance for the difference AUC of 0.05 between the two database conditions (P = .24; 95% confidence interval -0.03, 0.1). CONCLUSION These results demonstrate the robustness of our computerized classification system in the task of distinguishing between malignant and benign breast lesions on dynamic contrast-enhanced (DCE) MRI images from two manufacturers. Our study showed the feasibility of developing a computerized classification system that is robust across different scanners.