Application of a quantitative model to differentiate benign from malignant breast lesions detected by dynamic, gadolinium-enhanced MRI

Ktrans Calculation Using Reference Method Corrected Native T10 for Breast Cancer Diagnosis

Purpose

The objective of the study is to use multiple tube phantoms to generate correction factor at different spatial locations for each breast coil cuff to correct the native T10 value in the corresponding spatial location of the breast lesion. The corrected T10 value was used to compute Ktrans and analyze its diagnostic accuracy in the classification of target condition, i.e., breast tumors into malignant and benign.

Materials and Methods

Both in vitro phantom study (external reference) and patient's studies were acquired on simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) Biograph molecular magnetic resonance (mMR) system using 4 channel mMR breast coil. The spatial correction factors derived using multiple tube phantom were used for a retrospective analysis of dynamic contrast-enhanced (DCE) MRI data of 39 patients with a mean age of 50 years (31-77 years) having 51 enhancing breast lesions.

Results

Corrected and non-corrected receiver operating characteristic (ROC) curve analysis revealed a mean Ktrans value of 0.64 min-1 and 0.60 min-1, respectively. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy for non-corrected data were 86.21%, 81.82%, 86.20%, 81.81%, and 84.31%, respectively, and for corrected data were 93.10%, 86.36%, 90%, 90.47%, and 90.20% respectively. The area under curve (AUC) of corrected data was improved to 0.959 (95% confidence interval [CI] 0.862-0.994) from 0.824 (95% CI 0.694-0.918) of non-corrected data, and for NPV, it was improved to 90.47% from 81.81%, respectively.

Conclusion

T10 values were normalized using multiple tube phantom which was used for computation of Ktrans. We found significant improvement in the diagnostic accuracy of corrected Ktrans values that results in better characterization of breast lesions.

Use of Multiple-Tube Phantom: A Method to Globally Correct Native T1 Relaxation Time Inhomogeneity in Dedicated Molecular Magnetic Resonance Breast Coil

Background:

Native T1 relaxation time (T1₀) presents an important prerequisite to reliably quantify pharmacokinetic parameter like K^trans (volume transfer constant). Native T1 value can be varied because of the inhomogeneity in the breast coil, thus influencing the K^trans measurement.

Purpose:

The current study aims to design and use a phantom with multiple tubes for both breast cuffs to assess native T1 inhomogeneity across the dedicated molecular magnetic resonance (mMR) breast coil and adopt corrective method to spatially normalize T1 values to improve homogeneity.

Materials and Methods:

Two phantoms with multiple tubes (19 tubes) specially designed and filled with contrast medium with known T1 value were placed in each mMR breast coil cuff. Native T1 at various spatial locations was calculated applying dual flip angle sequence. Correction factors were derived at various spatial locations as a function of deviation of the native T1 value from phantom and applied to correct the native T1 relaxation time.

Results:

A statistically significant difference between native T1 values of the right and left anterior (P = 0.0095), middle (P = 0.0081), and posterior (P = 0.0004) parts of the breast coil. No significant difference was seen in the corrected T1 values between anterior (P = 0.402), middle (P = 0.305), and posterior (P = 0.349) aspects of both sides of the breast coil.

Conclusion:

Inhomogeneity in the native T1 value exists in dedicated mMR breast coil, and significant improvement can be achieved using specially designed external phantom with multiple tubes.

Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment

Breast cancer is the second leading cause of cancer death among US women, and the chance of a woman developing breast cancer sometime during her lifetime is one in eight. Early detection and diagnosis to allow appropriate locoregional and systemic treatment are key to improve the odds of surviving its diagnosis. Emerging data also suggest that different breast cancer subtypes (phenotypes) may respond differently to available adjuvant therapies. There is a growing understanding that not all patients benefit equally from systemic therapies, and therapeutic approaches are being increasingly personalized based on predictive biomarkers of clinical benefit. Optimal use of established and novel radiological imaging methods, such as magnetic resonance imaging and positron emission tomography, which have different biophysical mechanisms can simultaneously identify key functional parameters. These methods provide unique multiparametric radiological signatures of breast cancer, that will improve the accuracy of early diagnosis, help select appropriate therapies for early stage disease, and allow early assessment of therapeutic benefit.

The evolution of breast imaging: past to present

The practice of breast imaging has transitioned through a wide variety of technologic advances from the early days of direct-exposure film mammography to xeromammography to screen-film mammography to the current era of full-field digital mammography and digital breast tomosynthesis. Along with these technologic advances, organized screening, federal regulations based on the Mammography Quality Standards Act, and the development of the American College of Radiology Breast Imaging Reporting and Data System have helped to shape the specialty of breast imaging. With the development of breast ultrasonography and breast magnetic resonance imaging, both complementary to mammography, additional algorithms for diagnostic workup and screening high-risk subgroups of women have emerged. A substantial part of breast imaging practice these days also involves breast interventional procedures-both percutaneous biopsy to obtain tissue diagnosis and localization procedures to guide surgical excision. This article reviews the evolution of breast imaging starting from a historical perspective and progressing to the present day.

The effects of applying breast compression in dynamic contrast material-enhanced MR imaging

PURPOSE

To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions.

MATERIALS AND METHODS

This was a HIPAA-compliant, institutional review board-approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22-76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the noncompressed breast. Percentage enhancement difference was calculated between noncompressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-masslike enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed.

RESULTS

Mean percentage compression was 31.3% ± 9.2 (range, 5.8%-53.2%). Percentage enhancement was higher in noncompressed- versus compressed-breast studies in early (146% ± 66 vs 107% ± 42, respectively; P < .001) and delayed (158% ± 68 vs 107% ± 42, respectively; P = .1) phases. Among breast lesions, 12% (seven of 59) were significantly smaller when compressed, which led to underestimation of TNM classification (P < .001). Breast masses (n = 35) showed significantly higher early percentage enhancement (157% ± 71) than lesions with NMLE (n = 15, 120% ± 40; P = .02) and a percentage enhancement difference (47.5% ± 64 vs 17% ± 28, respectively; P = .023). Kinetic curve performance for identifying invasive cancer decreased after compression (area under ROC curve = 0.53 vs 0.71, respectively; P = .02). Breast compression resulted in complete loss of enhancement of nine of 210 lesions (4%).

CONCLUSION

Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.

Quadrature transmit coil for breast imaging at 7 tesla using forced current excitation for improved homogeneity

PURPOSE

To demonstrate the use of forced current excitation (FCE) to create homogeneous excitation of the breast at 7 tesla, insensitive to the effects of asymmetries in the electrical environment.

MATERIALS AND METHODS

FCE was implemented on two breast coils: one for quadrature (1) H imaging and one for proton-decoupled (13) C spectroscopy. Both were a Helmholtz-saddle combination, with the saddle tuned to 298 MHz for imaging and 75 MHz for spectroscopy. Bench measurements were acquired to demonstrate the ability to force equal currents on elements in the presence of asymmetric loading to improve homogeneity. Modeling and temperature measurements were conducted per safety protocol. B1 mapping, imaging, and proton-decoupled (13) C spectroscopy were demonstrated in vivo.

RESULTS

Using FCE to ensure balanced currents on elements enabled straightforward tuning and maintaining of isolation between quadrature elements of the coil. Modeling and bench measurements confirmed homogeneity of the field, which resulted in images with excellent fat suppression and in broadband proton-decoupled carbon-13 spectra.

CONCLUSION

FCE is a straightforward approach to ensure equal currents on multiple coil elements and a homogeneous excitation field, insensitive to the effects of asymmetries in the electrical environment. This enabled effective breast imaging and proton-decoupled carbon-13 spectroscopy at 7T.

Magnetic resonance in the detection of breast cancers of different histological types

Breast cancer incidence is increasing worldwide. Early detection is critical for long-term patient survival, as is monitoring responses to chemotherapy for management of the disease. Magnetic resonance imaging and spectroscopy (MRI/MRS) has gained in importance in the last decade for the diagnosis and monitoring of breast cancer therapy. The sensitivity of MRI/MRS for anatomical delineation is very high and the consensus is that MRI is more sensitive in detection than x-ray mammography. Advantages of MRS include delivery of biochemical information about tumor metabolism, which can potentially assist in the staging of cancers and monitoring responses to treatment. The roles of MRS and MRI in screening and monitoring responses to treatment of breast cancer are reviewed here. We rationalize how it is that different histological types of breast cancer are differentially detected and characterized by MR methods.

Discrimination of benign and malignant breast lesions by using shutter-speed dynamic contrast-enhanced MR imaging

PURPOSE

To assess the accuracy of the shutter-speed approach compared with standard approach dynamic contrast material-enhanced magnetic resonance (MR) imaging pharmacokinetic analysis for breast cancer diagnosis.

MATERIALS AND METHODS

This study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained from 89 high-risk women (age range, 28-83 years) who had 92 suspicious lesions with negative findings at mammography (but visible at MR imaging). Each underwent a research dynamic contrast-enhanced MR imaging examination just prior to a clinical MR imaging-guided interventional procedure. Tumor region of interest (ROI) averaged and (for some) pixel-by-pixel dynamic contrast-enhanced time-course data, together with mean arterial input function, were subjected to serial standard and shutter-speed approach analyses to extract pharmacokinetic parameters, including rate constant for passive contrast reagent transfer between plasma and interstitium (K(trans)) and interstitial space volume fraction, or v(e). Pathologic findings were used as reference standards. Diagnostic accuracy was assessed with receiver operating characteristic analyses.

RESULTS

The pathologic analyses revealed 20 malignant and 72 benign lesions. Positive predictive value of the institutional clinical breast MR imaging protocol was 22%. At 100% sensitivity, ROI-averaged shutter-speed approach K(trans) had significantly (P = .008) higher diagnostic specificity than standard approach K(trans): 86.1% versus 77.8%. The difference in the ROI-averaged K(trans) parameter value, or ΔK(trans) (≡ K(trans) [shutter-speed approach] - K(trans) [standard approach]), had even higher specificity (88.9%). Combined use of ROI analysis and pixel-by-pixel mapping of ΔK(trans) achieved 98.6% specificity at 100% sensitivity.

CONCLUSION

The use of the shutter-speed dynamic contrast-enhanced MR imaging method has the potential to improve breast cancer diagnostic accuracy and reduce putatively unnecessary biopsy procedures that yield benign pathologic findings.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11102413/-/DC1.

Optimization of time-to-peak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI

RATIONALE AND OBJECTIVES

The aim of this study was to investigate the feasibility of applying measures sensitive to time-to-peak (T(peak)) heterogeneity as indicators for malignancy on breast dynamic contrast-enhanced magnetic resonance imaging.

MATERIALS AND METHODS

The study included 39 benign and 97 malignant breast lesions from 103 patients. Lesions were automatically segmented by k-means clustering. Voxel-by-voxel T(peak) values were extracted using an empirical model. The pth percentile values (p = 10, 20…) of the T(peak) distribution within each lesion and the fractional and absolute hot spot volumes were determined, where the hot spot volume is the volume of tissue with T(peak) less than a threshold value. Using the area under the receiver-operating characteristic curve (AUC), these measures were tested as indicators for differentiating fibroadenomas from invasive lesions and from ductal carcinoma in situ, as well as for differentiating nonfibroadenoma benign lesions from these malignant lesions. Region of interest-based T(peak) measurements were also tested. Finally, the relationship between hot spot volume and lesion volume was investigated.

RESULTS

For differentiating fibroadenomas from malignant lesions, AUC values increased with decreasing values of p. At the optimal threshold (3 minutes), the hot spot volume provided high diagnostic performance (AUC ≥0.96 ± 0.02 for absolute hot spot volume). However, for differentiating nonfibroadenoma benign lesions from malignant lesions, AUC values were low. A significant correlation between absolute hot spot volume and lesion volume was found for malignant lesions and nonfibroadenoma benign lesions.

CONCLUSION

Quantitative analysis of the T(peak) distribution can be optimized for diagnostic performance, providing indicators sensitive to intralesion T(peak) heterogeneity.

Probability of malignancy for lesions detected on breast MRI: a predictive model incorporating BI-RADS imaging features and patient characteristics

OBJECTIVES

To predict the probability of malignancy for MRI-detected breast lesions with a multivariate model incorporating patient and lesion characteristics.

METHODS

Retrospective review of 2565 breast MR examinations from 1/03-11/06. BI-RADS 3, 4 and 5 lesions initially detected on MRI for new cancer or high-risk screening were included and outcomes determined by imaging, biopsy or tumor registry linkage. Variables were indication for MRI, age, lesion size, BI-RADS lesion type and kinetics. Associations with malignancy were assessed using generalized estimating equations and lesion probabilities of malignancy were calculated.

RESULTS

855 lesions (155 malignant, 700 benign) were included. Strongest associations with malignancy were for kinetics (washout versus persistent; OR 4.2, 95% CI 2.5-7.1) and clinical indication (new cancer versus high-risk screening; OR 3.0, 95% CI 1.7-5.1). Also significant were age > = 50 years, size > = 10 mm and lesion-type mass. The most predictive model (AUC 0.70) incorporated indication, size and kinetics. The highest probability of malignancy (41.1%) was for lesions on MRI for new cancer, > = 10 mm with washout. The lowest (1.2%) was for lesions on high-risk screening, <10 mm with persistent kinetics.

CONCLUSIONS

A multivariate model shows promise as a decision support tool in predicting malignancy for MRI-detected breast lesions.

Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer

Dynamic contrast-enhanced MRI (DCE-MRI) is an evolving tool for determining breast disease, which benefits from the move to imaging at 3 T. It has major capabilities for the diagnosis, detection and monitoring of malignancy. It benefits from being non-invasive and three-dimensional, allowing visualisation of the extent of disease and its angiogenic properties, visualisation of lesion heterogeneity, detection of changes in angiogenic properties before morphological alterations, and the potential to predict the overall response either before the start of therapy or early during treatment. In addition, DCE-MRI is emerging as a powerful tool for screening high-risk patients and for detecting high-grade ductal carcinoma in situ. However, there are also a number of limitations, including the overlap in enhancement patterns between malignant and benign disease, the failure to resolve microscopic disease particularly in the neoadjuvant setting, and the inconsistent predictive value of the enhancement pattern for clinical outcome. Careful consideration should be given to the technical requirements of individual examinations and the need for automation of post-processing techniques to appropriately handle the growing volume of data acquired. Research continues, focusing on the use of higher field strengths with improved spatial and temporal resolution data, improving understanding of the mechanism of contrast enhancement at the cellular level, and developing macromolecular and targeted contrast agents.

Recent advances in breast MRI and MRS

Breast MRI is an area of intense research and is fast becoming an important tool for the diagnosis of breast cancer. This review covers recent advances in breast MRI, MRS, and image post-processing and analysis. Several studies have explored a multi-parametric approach to breast imaging that combines analysis of traditional contrast enhancement patterns and lesion architecture with novel methods such as diffusion, perfusion, and spectroscopy to increase the specificity of breast MRI studies. Diffusion-weighted MRI shows some potential for increasing the specificity of breast lesion diagnosis and is even more promise for monitoring early response to therapy. MRS also has great potential for increasing specificity and for therapeutic monitoring. A limited number of studies have evaluated perfusion imaging based on first-pass contrast bolus tracking, and these clearly identify that vascular indices have great potential to increase specificity. The review also covers the relatively new acquisition technique of MR elastography for breast lesion characterization. A brief survey of image processing algorithms tailored for breast MR, including registration of serial dynamic images, segmentation and extraction of morphological features of breast lesions, and contrast uptake modeling, is also included. Recent advances in MRI, MRS, and automated image analysis have increased the utility of breast MR in diagnosis, screening, management, and therapy monitoring of breast cancer.

Molecular and functional imaging of breast cancer

Despite several major advances in breast cancer diagnosis and treatment, the American Cancer Society has estimated that in the US alone 43300 women and 400 men will die from breast cancer in 2007. Breast cancer typically is a multi-focal, multi-faceted disease, with the major cause of mortality being complications due to metastasis. Whereas a decade ago genetic alterations were the primary focus in cancer research, it is now apparent that the physiological tumor microenvironment, interactions between cancer cells and stromal cells such as endothelial cells, fibroblasts and macrophages, the extracellular matrix, and a multitude of secreted factors and cytokines influence progression, aggressiveness, and response of the disease to treatment. Prevention, early diagnosis, and treatment are the three broad challenges for MR molecular and functional imaging in reducing mortality from this disease. Multi-parametric molecular and functional MRI provides unprecedented opportunities for identifying novel targets for imaging and therapy at the bench, as well as for accurate diagnosis and monitoring response to therapy at the bedside. Here we provide an overview of the current status of molecular and functional MRI of breast cancer, outlining some key developments, as well as identifying some of the important challenges facing this field in the future.

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.

Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions

The value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in characterizing breast lesions on magnetic resonance imaging (MRI) was evaluated. Sixty-eight malignant and 34 benign lesions were included. In the scanning protocol, high temporal resolution imaging was combined with high spatial resolution imaging. The high temporal resolution images were recorded every 4.1 s during initial enhancement (fast dynamic analysis). The high spatial resolution images were recorded at a temporal resolution of 86 s (slow dynamic analysis). In the fast dynamic evaluation pharmacokinetic parameters (K^trans, V_e and k_ep) were evaluated. In the slow dynamic analysis, each lesion was scored according to the BI-RADS classification. Two readers evaluated all data prospectively. ROC and multivariate analysis were performed. The slow dynamic analysis resulted in an AUC of 0.85 and 0.83, respectively. The fast dynamic analysis resulted in an AUC of 0.83 in both readers. The combination of both the slow and fast dynamic analyses resulted in a significant improvement of diagnostic performance with an AUC of 0.93 and 0.90 (P = 0.02). The increased diagnostic performance found when combining both methods demonstrates the additional value of our method in further improving the diagnostic performance of breast MRI.

Meta-analysis of MR imaging in the diagnosis of breast lesions

PURPOSE

To determine, in a meta-analysis, the diagnostic performance of contrast material-enhanced magnetic resonance (MR) imaging in patients with breast lesions.

MATERIALS AND METHODS

Studies to assess the diagnostic performance of MR imaging in patients suspected of having breast cancer who underwent MR imaging and biopsy from January 1985 through March 2005 were reviewed for inclusion. A summary receiver operating characteristic curve was constructed, and pooled weighted estimates of sensitivity and specificity were calculated by using the recently developed bivariate approach for diagnostic meta-analysis.

RESULTS

Of 251 eligible studies, 44 were included in the meta-analysis (sample size range, 14-821; cancer prevalence, 23%-84%). Pooled weighted estimates of sensitivity and specificity were 0.90 (95% confidence interval: 0.88, 0.92) and 0.72 (95% confidence interval: 0.67, 0.77), respectively. The performance of breast MR imaging was influenced by the prevalence of cancer in the studied population (P = .05) and by whether two criteria (ie, morphology, enhancement, and kinetic enhancement pattern)--versus one or three criteria--were used to differentiate benign from malignant lesions (P = .02).

CONCLUSION

MR imaging of the breast has high sensitivity and lower specificity in the evaluation of breast lesions.

SUPPLEMENTAL MATERIAL

http://radiology.rsnajnls.org/cgi/content/full/2461061298/DC1.

A high spatial resolution 1H magnetic resonance spectroscopic imaging technique for breast cancer with a short echo time

The high sensitivity but low specificity of breast MRI has prompted exploration of breast (1)H MRS for breast cancer detection. However, several obstacles still prevent the routine application of in vivo breast (1)H MRS, including poor spatial resolution, long acquisition time associated with conventional multi-voxel MRS imaging (MRSI) techniques, and the difficulty of "extra" lipid suppression in a magnetic field with relatively poor achievable homogeneity compared to the brain. Using a combination of a recently developed echo-filter (EF) suppression technique and an elliptical sampling scheme, we demonstrate the feasibility of overcoming these difficulties. It is robust (the suppression technique is insensitive to magnetic field inhomogeneity), fast (acquisition time of about 12 min) and offers high spatial resolution (up to 0.6 cm(3) per voxel at 1.5 T with a TE of only 60 ms). This approach should be even better at 3 T with higher resolution and/or shorter TE.

Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphologic features

PURPOSE

To compare the diagnostic performance of breast lesions by the enhancement patterns and morphologic criteria on magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Both T1-weighted 3D gradient-echo sequences with high temporal resolution and high-spatial-resolution MRI were performed on 190 patients with a total of 204 enhancing lesions (144 malignant and 60 benign). The enhancement patterns and morphologic features of each mass and nonmass lesion were analyzed, and the diagnostic performance was compared.

RESULTS

The sensitivity and specificity of the morphologic criteria were statistically significantly higher than those of the enhancement patterns (sensitivity: P = 0.0012, specificity: P = 0.0003), and the A(z) values for the three observers were 0.900, 0.919, and 0.900. The diagnostic accuracy of the morphologic criteria for both types of lesions was superior, and the differences were statistically significant (mass: P = 0.0001, nonmass: P = 0.0389).

CONCLUSION

The analysis of the morphologic features of enhancing breast lesions alone showed higher diagnostic performance; therefore, signal intensity (SI) time-course data may not be needed to diagnose malignant breast lesions.

Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy

RATIONALE AND OBJECTIVES

The objective of this work was to develop a quantitative method for improving lesion detection in dynamic contrast-enhanced magnetic resonance mammography (DCEMRM). For this purpose, we segmented and analyzed suspicious regions according to their contrast enhancement dynamics, generated a normalized maximum intensity-time ratio (nMITR) projection, and explored it to extract important features, to improve accuracy and reproducibility of detection.

MATERIALS AND METHODS

A novel automated method is introduced to segment and analyze lesions in three dimensions. It consists of four consecutive stages: volume of interest selection, nMITR projection generation using a voxel sampling method based on a moving 3 x 3 mask, three-dimensional lesion segmentation, and feature extraction. The nMITR projection of the detected lesion is used to extract six features: mean, maximum, standard deviation, kurtosis, skewness, and entropy, and their diagnostic significance is studied in detail. High-resolution MR images of 52 breast masses from 46 women are analyzed using the technique developed.

RESULTS

Entropy, standard deviation, and the maximum and mean value features were found to have high significance (P < 0.001) and diagnostic accuracy (0.86-0.97). The kurtosis and skewness were not significant. Automated analysis of DCEMRM using nMITR was shown to be feasible.

CONCLUSION

The lesion detection method described is efficient and leads to improved, accurate, reproducible diagnoses. It is reliable in terms of observer variability and may allow for a better standardization of clinical evaluations. The findings demonstrate the usefulness of nMITR based features; nMITR-entropy shows the best performance for quantitative diagnosis.

Breast carcinomas with strong high-signal intensity on T2-weighted MR images: Pathological characteristics and differential diagnosis

PURPOSE

To investigate the histopathological characteristics of breast carcinomas with strong high-signal intensity (SHi) on T2-weighted (T2W) MR images (T2-SHi), and discuss the differential diagnosis between T2-SHi breast carcinomas and T2-SHi fibroadenomas.

MATERIALS AND METHODS

Thirty of 480 breast carcinomas examined by MRI were defined as tumors with T2-SHi (defined as homogeneous higher signal intensity (SI) compared to surrounding normal breast tissue on fat-suppressed T2W imaging (T2WI). They included eight mucinous and 22 nonmucinous carcinomas. The histopathological characteristics of T2-SHi breast carcinomas, their signal-to-noise ratios (SNRs) on T2WI, contrast-enhancement patterns, and morphology were compared with those of 22 non-T2-SHi breast carcinomas and 19 T2-SHi fibroadenomas.

RESULTS

In nonmucinous carcinomas T2-SHi was attributable to a mixture of background matrix, a higher proportion of cells than stroma, abundant cytoplasm, edematous stroma, and hemorrhage. The significantly high SNR (mean = 75) and enhancing internal septations seen in mucinous carcinomas, and the washout phenomenon, irregular border, absence of internal septation, and rim enhancement seen in nonmucinous carcinomas provide useful information for differentiating these tumors from T2-SHi fibroadenomas.

CONCLUSION

A mixture of several histopathological characteristics was associated with T2-SHi breast carcinomas. The combined information from T2WI and contrast-enhanced (CE) imaging may help distinguish T2-SHi breast carcinomas from T2-SHi fibroadenomas.

Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced MR mammography

PURPOSE

To evaluate the three-time-point (3TP) method for diagnosis of breast lesions detected on contrast-enhanced MR mammography.

MATERIALS AND METHODS

MR imaging was performed in 40 women with 120 suspected breast lesions in mammography and/or sonography. The contrast kinetics was converted by 3TP software on a pixel-by-pixel basis into color-coded images. Lesion diagnosis was made by analysis of color intensity and color hue. The 3TP results were compared with the results of the region-of-interest (ROI) method. In 16 patients, we were able to correlate the results with histopathological findings.

RESULTS

The 3TP method could successfully be performed in all MR mammographies. Forty (33%) lesions had a diameter of less than 5 mm, 56 (47%) lesions between 5 and 10 mm, and 24 (20%) lesions were greater than 1 cm. Of all 120 lesions, 65 (54%) showed heterogeneous contrast enhancement. In 117 (97%) of all 120 lesions the results of ROI and the automated 3TP method were considered equivalent. However, in three lesions the manual ROI differed from the 3TP method. After a second, repeated manual ROI placement, we were able to confirm equivalent results with the 3TP images as well.

CONCLUSIONS

The 3TP method automatically and reliably converts contrast kinetic information of the entire breast into a color-coded image. The 3TP method presents kinetic information of the entire dynamic series in an easy-to-interpret format and this automated method may allow to forego time-consuming and sometimes subjective manual ROI placements. This method displays the heterogeneity of the contrast enhancement pattern often observed in malignant lesions and makes it usable as diagnostic criterion.

Promising techniques for breast cancer detection, diagnosis, and staging using non-ionizing radiation imaging techniques

Traditional imaging for the diagnosis and staging of breast cancer has relied on the tissue morphology of cancers in the background of normal patterns of fibroglandular breast tissue. X-ray mammography and ultrasound have been the primary modalities for the diagnosis and the work-up of breast cancer. New modalities have been validated including magnetic resonance imaging (MRI) and positron emission tomography (PET). New pulse sequences in MRI combined with contrast enhancement kinetic perfusion curves have greatly enhanced detection of mammographically occult cancers. New modalities on the horizon include optical imaging, exploiting again the differential perfusion properties of cancers in a background of normal glandular tissue. Even more specificity can be ach eved with the addition of ductal or intravenous introduction of optical probes specific to tumor associated antigens such as the HER-2/neu receptor in aggressive breast cancers. Quantum dots and other fluorescent dyes coupled to peptides or other probes will greatly enhance our ability to detect cancers earlier and without ionizing radiation.

Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study

PURPOSE

To prospectively determine the prevalence and predictive value of three-dimensional (3D) and dynamic breast magnetic resonance (MR) imaging and contrast material kinetic features alone and as part of predictive diagnostic models.

MATERIALS AND METHODS

The study protocol was approved by the institutional review board or ethics committees of all participating institutions, and informed consent was obtained from all participants. Although study data collection was performed before HIPAA went into effect, standards that would be compliant with HIPAA were adhered to. Data from the International Breast MR Consortium trial 6883 were used in the analysis. Women underwent 3D (minimum spatial resolution, 0.7 x 1.4 x 3 mm; minimal temporal resolution, 4 minutes) and dynamic two-dimensional (temporal resolution, 15 seconds) MR imaging examinations. Readers rated enhancement shape, enhancement distribution, border architecture, enhancement intensity, presence of rim enhancement or internal septations, and the shape of the contrast material kinetic curve. Regression was performed for each feature individually and after adjustment for associated mammographic findings. Multivariate models were also constructed from multiple architectural and dynamic features. Areas under the receiver operating characteristic curve (Az values) were estimated for all models.

RESULTS

There were 995 lesions in 854 women (mean age, 53 years +/- 12 [standard deviation]; range, 18-80 years) for whom pathology data were available. The absence of enhancement was associated with an 88% negative predictive value for cancer. Qualitative characterization of the dynamic enhancement pattern was associated with an Az value of 0.66 across all lesion architectures. Focal mass margins (Az = 0.76) and signal intensity (Az = 0.70) were highly predictive imaging features. Multivariate models were constructed with an Az value of 0.880.

CONCLUSION

Architectural and dynamic features are important in breast MR imaging interpretation. Multivariate models involving feature assessment have a diagnostic accuracy superior to that of qualitative characterization of the dynamic enhancement pattern.

Dynamic MRI with projection reconstruction and KWIC processing for simultaneous high spatial and temporal resolution

A method for dynamic imaging in MRI is presented that enables the acquisition of a series of images with both high temporal and high spatial resolution. The technique, which is based on the projection reconstruction (PR) imaging scheme, utilizes distinct data acquisition and reconstruction strategies to achieve this simultaneous capability. First, during acquisition, data are collected in multiple undersampled passes, with the view angles interleaved in such a way that those of subsequent passes bisect the views of earlier ones. During reconstruction, these views are weighted according to a previously described k-space weighted image contrast (KWIC) technique that enables the manipulation of image contrast by selective filtering. Unlike conventional undersampled PR methods, the proposed dynamic KWIC technique does not suffer from low image SNR or image degradation due to streaking artifacts. The effectiveness of dynamic KWIC is demonstrated in both simulations and in vivo, high-resolution, contrast-enhanced imaging of breast lesions.

Detection of breast malignancy: diagnostic MR protocol for improved specificity

PURPOSE

To prospectively determine if a combined magnetic resonance (MR) protocol that includes T1-weighted dynamic contrast agent-enhanced (DCE) MR imaging, hydrogen 1 (1H) MR spectroscopy, and T2*-weighted perfusion MR imaging improves specificity in the diagnosis of breast cancer.

MATERIALS AND METHODS

The combined MR imaging-MR spectroscopy protocol was performed in 50 patients after positive findings at mammography but prior to biopsy. Single-voxel proton MR spectroscopy and perfusion MR imaging were conducted only if DCE MR images showed rapid contrast enhancement in the lesion. Biopsy results were used as the reference for comparison with MR results and for calculation of sensitivity and specificity in the detection of breast malignancy.

RESULTS

DCE MR imaging alone showed 100% sensitivity and 62.5% specificity. The specificity improved to 87.5% with the addition of 1H MR spectroscopy and to 100% with the further addition of perfusion MR imaging. Twenty-eight patients underwent both MR spectroscopy and perfusion MR imaging. Two patients underwent MR spectroscopy but declined to undergo perfusion MR imaging. The remaining 20 patients had negative results at DCE MR imaging and therefore did not undergo the additional examinations.

CONCLUSION

The combined MR protocol of DCE MR imaging, 1H MR spectroscopy, and perfusion MR imaging has high sensitivity and specificity in the diagnosis of breast cancer.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

The effect of varying spectral resolution on the quality of high spectral and spatial resolution magnetic resonance images of the breast

PURPOSE

To evaluate the effect of varying spectral resolution on image quality of high spectral and spatial resolution (HiSS) images.

MATERIALS AND METHODS

Eight women with suspicious breast lesions and six healthy volunteers were scanned using echo-planar spectroscopic imaging (EPSI) at 1.5 Tesla with 0.75- to 1-mm in-plane resolution and 2.3- to 2.6-Hz spectral resolution. Time domain data were truncated to obtain proton spectra in each voxel with varying (2.6-83.3 Hz) resolution. Images with intensity proportional to water signal peak heights were synthesized. Changes in water signal line shape following contrast injection were analyzed.

RESULTS

Fat suppression is optimized at approximately 10-Hz spectral resolution and is significantly improved by removal of wings of the fat resonance. This was accomplished by subtracting a Lorentzian fit to the fat resonance from the proton spectrum. The water resonance is often inhomogeneously broadened, and very high spectral resolution is necessary to resolve individual components. High spectral resolution is required for optimal contrast in anatomic features with very high T(2)* (e.g., within a lesion) and for detection of often subtle effects of contrast agents on water signal line shape.

CONCLUSION

Despite a trade-off between the spectral resolution and signal-to-noise ratio, it is beneficial to acquire data at the highest spectral resolution currently attainable at 1.5 Tesla.

Improvement in breast lesion characterization with dynamic contrast-enhanced MRI using pharmacokinetic modeling and bookendT1 measurements

Dynamic contrast-enhanced breast MR imaging was performed on 14 patients (five cancerous lesions, nine benign) with slice-selective spoiled gradient-recalled echo (2D SPGR) imaging. Adiabatic saturation recovery T(1) measurements were performed before (T(1pre)) and after (T(1post)) 2D SPGR imaging. These two "bookend" T(1) measurements were used to calibrate the equations which were employed to convert the time course of the 2D SPGR signal strength to T(1)-vs.-time, which in turn was used to compute the gadolinium concentration-vs.-time ([C](t)) in the lesion. The extraction-flow product (EF) was computed for each lesion by pharmacokinetic modeling of [C](t). For this study, EF provided a sensitivity and specificity for cancer of 100% and 78%, respectively. When only T(1pre) was used to estimate [C](t) (which assumes a priori knowledge of the shape and amplitude of the slice profile), the sensitivity and specificity fell to 80% and 56%, respectively. This is presumably due to unexpected variations in the shape and/or amplitude of the slice profile, which could be caused by factors such as patient-to-patient variations in breast geometry or inconsistently set transmit gains. Therefore, both T(1pre) and T(1post) measurements are necessary for optimum sensitivity and specificity using pharmacokinetic analysis.

Benign and Malignant Breast Lesions: Diagnosis with Multiparametric MR Imaging

PURPOSE

To both develop and use a tissue signature method for the identification and classification of breast lesions and healthy breast tissue with magnetic resonance (MR) imaging.

MATERIALS AND METHODS

Thirty-six patients underwent breast MR imaging (T1- and T2-weighted imaging and three-dimensional T1-weighted imaging with and without contrast material enhancement), followed by biopsy or mastectomy and histopathologic analysis. Tissue cluster analysis was performed by using the iterative self-organizing data technique to identify glandular, adipose, and lesion tissue signature vectors. Glandular and lesion tissue vectors were characterized by angular separation from the reference adipose tissue vector. Differences in angular separation of histologically proved benign and malignant lesion groups were evaluated with an independent t test. The usefulness of the angular separation model for distinguishing benign from malignant lesions was evaluated with nonparametric receiver operating characteristic curve analysis.

RESULTS

The model enabled successful identification and characterization of breast lesion tissue clusters in all patients; 18 lesions were benign, and 18 were malignant. Angular separation +/- SD was 17.8 degrees +/- 6.1 degrees between adipose tissue and malignant lesions and 29.0 degrees +/- 11.2 degrees between adipose tissue and benign lesions. Angular separations of benign lesions and malignant lesions were significantly different (P =.002), with a specificity of 78% and sensitivity of 89% at a cutoff value of 21 degrees. Significant differences in angular separation from adipose tissue also were found between glandular tissue and lesion tissue (P <.001) and, in glandular tissue, between patients with benign lesions and those with malignant lesions (P =.04). The area under the receiver operating characteristic curve was 0.84.

CONCLUSION

Multispectral analysis of conventional breast MR images based on the iterative self-organizing data model and on measurement of angular separation between tissue signature vectors may enable automated lesion identification and classification.

Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks

PURPOSE

To automate the diagnosis of malignancy by classifying breast tissues as negative or positive for malignancy in gadolinium-enhanced dynamic magnetic resonance (MR) images, using static region descriptors and a neural network classifier.

MATERIALS AND METHODS

We propose a novel approach whereby the classifier evaluates a number of parameters that identify important tumor characteristics, as obtained by digital image processing techniques. These parameters include static signal intensity (SI) after contrast enhancement, mass margin descriptors, evaluation of mass shape by calculation of eccentricity, mass size, and mass granularity by texture analysis. Datasets for 14 patients were obtained by use of the 1.5T PMRTOW Clinical Imager.

RESULTS

Statistical performance evaluation of the neural networks indicated 90%-100% sensitivity, 91%-100% specificity, and 91%-100% accuracy.

CONCLUSION

Although this work is preliminary, it may reduce overall health-care time and costs, and enable higher accuracy in automated breast cancer detection systems.

Independent component analysis for the examination of dynamic contrast-enhanced breast magnetic resonance imaging data: preliminary study

RATIONALE AND OBJECTIVE

An application of independent component analysis (ICA) is reported for the detection and characterization of breast lesions in dynamic contrast-enhanced MRI. The ICA technique, which uses a novel statistical algorithm to separate mixed signal sources of unknown nature, enables the extraction of spatial and temporal features of dynamic MRI data.

MATERIALS AND METHODS

Six patients with confirmed lesion diagnosis (three with malignancy and three with benign condition) participated in this study. T1-weighted MRI covering both breast volumes was dynamically acquired in every 90 seconds after the bolus injection of contrast media using three-dimensional fast low-angle shot sequence (3-D FLASH).

RESULTS

With the application of the ICA, differential signal responses were delineated from benign and malignant lesions, and areas with high temporal correlations with the extracted signal components were selectively visualized.

CONCLUSIONS

The results suggest that ICA allowed for an identification of lesion morphology and lesion-specific dynamic enhancement patterns.

Clinical testing of high-spatial-resolution parametric contrast-enhanced MR imaging of the breast

OBJECTIVE

We performed a prospective clinical test of a high-spatial-resolution model-based parametric method for diagnosis of breast lesions detected on contrast-enhanced MR imaging.

SUBJECTS AND METHODS

Fifty-seven women with 68 pathologically confirmed breast lesions were imaged (45 masses, 23 microcalcifications). Seven consecutive 2-min three-dimensional gradient-recalled echo acquisitions were performed after suitably timed gadopentetate dimeglumine injections. We derived a composite parametric image from three judiciously selected time points (three-time-point method), using a model-based kinetic algorithm. In this composite image, color brightness and hue signify contrast uptake and washout characteristics related to the product of microvessel surface area and permeability, as well as to the extracellular volume fraction. The reviewer was provided with the slice location of lesions, but with no other radiographic or clinical information. The reviewer then classified the lesions as benign or malignant using a 5-step receiver operating characteristic scale.

RESULTS

Observers using the three-time-point method correctly diagnosed 27 of 31 malignant and 31 of 37 benign lesions (sensitivity, 87%; specificity, 84%). The area under the receiver operating characteristic curve was 0.911. False-negative results were found for three patients with low- to intermediate-grade ductal carcinoma in situ and one patient with 5-mm invasive ductal cancer. For the 45 solid lesions, sensitivity and specificity were 96% and 82%, respectively.

CONCLUSION

Application of the three-time-point method permitted, in most cases, differentiation of malignant and benign lesions, even in the presence of complex breast enhancement patterns. Sensitivity for solid tumors was higher than for ductal carcinoma in situ.

Comparison between radiological and artificial neural network diagnosis in clinical screening

The imaging protocol of the UK multicentre magnetic resonance imaging study for screening in women at genetic risk of breast cancer aims to assist in detecting and diagnosing malignant breast lesions. In this paper, we evaluate a three-layer, feed-forward, backpropagation neural network as an artificial radiological classifier using receiver operating characteristic (ROC) curve analysis and compare the results with those obtained using a proposed radiological scoring system for the study which currently supplements the radiologist's clinical opinion, in comparison with histological diagnosis. Based on the 76 symptomatic cases evaluated, descriptive features scored by radiologists showed considerable overlap between benign and malignant, although some features such as irregular contours and heterogeneous enhancement were more often associated with malignant pathology. In this preliminary evaluation, ROC analysis showed that the proposed scoring scheme did not perform well, indicating further refinement is required. When all 23 features were used in the neural network, its performance was poorer than that of the scoring scheme. When only ten features were used, limited to descriptors of enhancement characteristics, the neural network performed similar to the scoring scheme. This comparison shows that the neural network approach to clinical diagnosis has considerable potential and warrants further development.

Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging

PURPOSE

To evaluate the value of diffusion-weighted imaging (DWI) in distinguishing between benign and malignant breast lesions.

MATERIALS AND METHODS

Fifty-two female subjects (mean age = 58 years, age range = 25-75 years) with histopathologically proven breast lesions underwent DWI of the breasts with a single-shot echo-planar imaging (EPI) sequence using large b values. The computed mean apparent diffusion coefficients (ADCs) of the breast lesions and cell density were then correlated.

RESULTS

The ADCs varied substantially between benign breast lesions ((1.57 +/- 0.23) x 10(-3) mm(2)/second) and malignant breast lesions ((0.97 +/- 0.20) x 10(-3) mm(2)/second). In addition, the mean ADCs of the breast lesions correlated well with tumor cellularity (P < 0.01, r = -0.542).

CONCLUSION

The ADC would be an effective parameter in distinguishing between malignant and benign breast lesions. Further, tumor cellularity has a significant influence on the ADCs obtained in both benign and malignant breast tumors.

Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network

A neural network system was designed to extract and analyze the quantitative data from time-intensity profile. These data was used to predict the outcome of biopsy in a group of patients with histopathologically proved breast lesions. The performance of an artificial neural network (ANN) was compared with radiologists using a database with 120 patients' records each of which consisted of 14 quantitative parameters mostly derived directly from time-intensity profile. The network was trained and tested using the jackknife method and its performance was then compared with that of the radiologists in terms of sensitivity, specificity and accuracy using receiver operating characteristic curve (ROC) analysis. The network was able to classify correctly 107 of 120 original cases and yielded a better diagnostic accuracy (89%), compared with that of the radiologist (79%) by performing a constructive association between extracted quantitative data and corresponding pathological results (r=0.72, P<0.001).

Simultaneous acquisition of multiple resolution images for dynamic contrast enhanced imaging of the breast

An imaging technique is described that allows the reconstruction of a series of images at high temporal rates, while simultaneously providing images at high spatial resolution. The method allows one to arbitrarily choose from among several combinations of temporal/spatial resolutions during postprocessing. This flexibility is accomplished by strategically interleaving multiple undersampled projection reconstruction datasets (or subapertures), in which each set can be used to reconstruct a high temporal resolution image. Images with increasingly higher spatial resolutions can subsequently be formed by combining two or more subaperture datasets. The technique is demonstrated in vivo to assess the kinetics of contrast enhancement and to visualize the architectural features of suspicious breast lesions.

Overview of breast imaging

In summary, it is an exciting time in breast imaging with many tools being brought to bear on an ever more common problem. The challenge for this decade will be to develop optimal cost-effective strategies to use all the tools now available with minimal discomfort and disfigurement to the patient.

Critical role of spatial resolution in dynamic contrast-enhanced breast MRI

The spatial resolution of three-dimensional (3D) gradient-echo T1-weighted images, from 40 women with 25 malignant and 23 benign lesions, was purposely degraded to determine the role of spatial resolution in recording, analysis, and diagnosis of dynamic contrast-enhanced breast MRI. Images were recorded and analyzed at pixel resolution according to the 3TP method (Degani et al., Nat Med 1997;3:780-782). Reduction in spatial resolution degraded the appearance of foci with fast wash-in and fast washout dynamics. This resulted in an increase in false-negative diagnoses. The sensitivity for differentiating between malignant and benign lesions, using threshold criteria defined by the 3TP analysis, of 76% decreased to 60% and 24% for a 2- and 4-fold reduction in spatial resolution, respectively, without affecting significantly the high specificity (96-100%). In order to minimize false-negative diagnoses of contrast-enhanced breast MRI and maintain high specificity, it is essential to record and analyze the dynamic behavior at high spatial resolution. J. Magn. Reson. Imaging 2001;13:862-867.

Development, standardization, and testing of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging studies

The purpose of this study was to develop, standardize, and test reproducibility of a lexicon for reporting contrast-enhanced breast magnetic resonance imaging (MRI) examinations. To standardize breast MRI lesion description and reporting, seven radiologists with extensive breast MRI experience developed consensus on technical detail, clinical history, and terminology reporting to describe kinetic and architectural features of lesions detected on contrast-enhanced breast MR images. This lexicon adapted American College of Radiology Breast Imaging and Data Reporting System terminology for breast MRI reporting, including recommendations for reporting clinical history, technical parameters for breast MRI, descriptions for general breast composition, morphologic and kinetic characteristics of mass lesions or regions of abnormal enhancement, and overall impression and management recommendations. To test morphology reproducibility, seven radiologists assessed morphology characteristics of 85 contrast-enhanced breast MRI studies. Data from each independent reader were used to compute weighted and unweighted kappa (kappa) statistics for interobserver agreement among readers. The MR lexicon differentiates two lesion types, mass and non-mass-like enhancement based on morphology and geographical distribution, with descriptors of shape, margin, and internal enhancement. Lexicon testing showed substantial agreement for breast density (kappa = 0.63) and moderate agreement for lesion type (kappa = 0.57), mass margins (kappa = 0.55), and mass shape (kappa = 0.42). Agreement was fair for internal enhancement characteristics. Unweighted kappa statistics showed highest agreement for the terms dense in the breast composition category, mass in lesion type, spiculated and smooth in mass margins, irregular in mass shape, and both dark septations and rim enhancement for internal enhancement characteristics within a mass. The newly developed breast MR lexicon demonstrated moderate interobserver agreement. While breast density and lesion type appear reproducible, other terms require further refinement and testing to lead to a uniform standard language and reporting system for breast MRI. J. Magn. Reson. Imaging 2001;13:889-895.

Quantitative gadopentetate-enhanced MRI of breast tumors: testing of different analytic methods

This study assessed several proposed imaging strategies and analytic methods based on gadopentetate-enhanced MRI to differentiate benign from malignant breast tumors in a blinded experimental animal study. Steady-state dynamic MRI and first-pass imaging, performed with either T(1)- or T*(2)- weighted sequences, were compared. Semiquantitative and quantitative analysis methods, based on empirical measures of the data or physiological models, were subsequently applied to the imaging datasets. Comparative measures provided pathologic distinction of benign from malignant tumors, tumor grading, and histologic determination of microvascular density. Of the eight tested methods, only one, an estimate of first-pass perfusion using T *(2)-weighted imaging, showed an almost significant (P = 0.05) difference between benign and malignant tumors and correlated almost significantly (r =.3, P = 0.06) with the tumor grade. All other tests, performed either with steady-state imaging or with T(1)-weighted first-pass imaging, failed to differentiate benign from malignant tumors. In addition, they yielded poor correlations with tumor grade and microvascular density.