Development of low-dose photon-counting contrast-enhanced tomosynthesis with spectral imaging

PURPOSE

To demonstrate the feasibility of low-dose photon-counting tomosynthesis in combination with a contrast agent (contrast material-enhanced tomographic mammography) for the differentiation of breast cancer.

MATERIALS AND METHODS

All studies were approved by the institutional review board, and all patients provided written informed consent. A phantom model with wells of iodinated contrast material (3 mg of iodine per milliliter) 1, 2, 5, 10, and 15 mm in diameter was assessed. Nine patients with malignant lesions and one with a high-risk lesion (atypical papilloma) were included (all women; mean age, 60.7 years). A multislit photon-counting tomosynthesis system was utilized (spectral imaging) to produce both low- and high-energy tomographic data (below and above the k edge of iodine, respectively) in a single scan, which allowed for dual-energy visualization of iodine. Images were obtained prior to contrast material administration and 120 and 480 seconds after contrast material administration. Four readers independently assessed the images along with conventional mammograms, ultrasonographic images, and magnetic resonance images. Glandular dose was estimated.

RESULTS

Contrast agent was visible in the phantom model with simulated spherical tumor diameters as small as 5 mm. The average glandular dose was measured as 0.42 mGy per complete spectral imaging tomosynthesis scan of one breast. Because there were three time points (prior to contrast medium administration and 120 and 480 seconds after contrast medium administration), this resulted in a total dose of 1.26 mGy for the whole procedure in the breast with the abnormality. Seven of 10 cases were categorized as Breast Imaging Reporting and Data System score of 4 or higher by all four readers when reviewing spectral images in combination with mammograms. One lesion near the chest wall was not captured on the spectral image because of a positioning problem.

CONCLUSION

The use of contrast-enhanced tomographic mammography has been demonstrated successfully in patients with promising diagnostic benefit. Further studies are necessary to fully assess diagnostic sensitivity and specificity.

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.

[Consensus Meeting of Course Directors in Breast Imaging, 9 May 2009, in Frankfurt am Main - Topic: Masses]

Fourteen directors of breast imaging courses met on behalf of the German Society of Radiology to discuss the actual standards of the diagnosis of masses. Open questions on the fields of mammography, breast ultrasound and breast MRI were elucidated by a presentation of one of the 14 panel members, followed by an electronic anonymous voting. Beside of the panel members, more than 300 participants of the consensus meeting contributed their opinions by electronic voting. Answers with definite majorities (> 75 %), answers with narrow majorities and those with missing majorities were discriminated from each other. The opinions of the plenum were compared to the majorities of the podium and showed only a few critical differences. Due to unequivocal majorities seven answers to important questions concerning the diagnosis of masses can lead to a further standardization of breast cancer diagnosis.

Prevalence Rates of Pathogenic Mutations in the BRCA1 and BRCA2 Genes in Families with Different Disease Histories: Results from the German Consortium for Hereditary Breast and Ovarian Cancer

Introduction: In the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), a large number of families with different histories of breast and ovarian cancers was screened for pathogenic mutations in the tumor-suppressor genes BRCA1/2. We here aimed to analyse the prevalence rates of pathogenic mutations for various familial disease histories.Methods: From 1997-2009 more than 6,000 families were screened for pathogenic mutations in BRCA1/2. Mutation analysis was performed by DHPLC (denaturing high-performance liquid chromatography) pre-screening, direct sequencing of suspect fragments and MLPA (multiplex ligation-dependant probe amplification) in at least one index case of these families. Complete pedigree information over three generations was used for the analysis. All lineages within a pedigree were traced and checked for the presence of several predefined disease histories.Results: Detailed mutation prevalence rates have been determined for a variety of familial risk situations. The highest mutation prevalence rate (78%, 95%CI 67-87%) was seen in families with at least two relatives affected by pre-menopausal breast cancer and at least two affected by ovarian cancer at any age. Importantly, the presence of a single woman affected by breast and ovarian cancer or bilateral breast cancer with first breast cancer under the age of 41 was associated with a BRCA1/2 mutation prevalence of 53% (95%CI 48-59%) and 34% (95%CI 23-47%) respectively. Interestingly, low mutation prevalence rates of <10% were found in families with only one case of male breast cancer (9%, 95%CI 4-19%), and in families with three or more cases of postmenopausal breast cancer (7%, 95%CI 4-14%), the former being only BRCA2 mutations. Overall, the mutation prevalence was 27% (95% CI 26-29%) in the whole cohort.Conclusion: We provide comprehensive estimates for BRCA1/2 mutation prevalence rates that are based on a large number of families with comprehensive pedigree information. Such estimates can be used to develop cost effective screening strategies for public health care providers.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4073.

Contralateral breast cancer risk in BRCA1 and BRCA2 mutation carriers

PURPOSE

To estimate the risk for contralateral breast cancer in members of BRCA1- and BRCA2-positive families and to determine predictive risk factors.

PATIENTS AND METHODS

A retrospective, multicenter, cohort study was performed from 1996 until 2008 and comprised 2,020 women with unilateral breast cancer (index patients, n = 978; relatives, n = 1.42) from 978 families who had a BRCA1 or BRCA2 mutation. Cox regression analysis was applied to assess the association of age at first breast cancer with time from first to contralateral breast cancer, stratified by the affected BRCA gene.

RESULTS

The cumulative risk for contralateral breast cancer 25 years after first breast cancer was 47.4% (95% CI, 38.8% to 56.0%) for patients from families with BRCA1 or BRCA2 mutations. Members of families with BRCA1 mutations had a 1.6-fold (95% CI, 1.2-fold to 2.3-fold) higher risk of contralateral breast cancer than members of families with BRCA2 mutations. Younger age at first breast cancer was associated with a significantly higher risk of contralateral breast cancer in patients with BRCA1 mutation, and a trend was observed in patients with BRCA2 mutation. After 25 years, 62.9% (95% CI, 50.4% to 75.4%) of patients with BRCA1 mutation who were younger than 40 years of age at first breast cancer developed contralateral breast cancer, compared with only 19.6% (95% CI, 5.3% to 33.9%) of those who were older than 50 years of age at first breast cancer.

CONCLUSION

Contralateral breast cancer risk depends on age at first breast cancer and on the affected BRCA gene, and this risk should be considered in treatment planning.

Near-infrared laser computed tomography of the breast first clinical experience

RATIONALE AND OBJECTIVES

The purpose of the present study was to evaluate a near-infrared (NIR) laser breast imaging system (Computed Tomography Laser Mammography [CTLM]) as an adjunct to mammography by means of receiver-operating characteristic (ROC) analysis. The NIR technique used in this study is based on the absorption of NIR light by hemoglobin. Malignant tumors can be detected by imaging their neovascularization.

MATERIALS AND METHODS

Eighty-two patients were examined by both CTLM and mammography. Seventy-nine of the 82 patients underwent biopsies, and three patients had 2-year follow up. Three-dimensional scans were acquired with an NIR laser computed tomographic scanner (the CTLM system) at a slice thickness of 4 mm. Mammograms were analyzed alone and together with CTLM images.

RESULTS

Histology revealed 37 benign and 42 malignant lesions. For the combination of mammography and CTLM, the area under the ROC curve was significantly larger than for mammography alone. In addition, it was shown that the difference in area under the ROC curve between the combination of both methods and mammography alone was considerably larger for dense breasts than for radiolucent breasts, although these differences were not statistically significant.

CONCLUSION

CTLM, used as an adjunct, may serve as a feasible tool to improve the diagnostic capabilities of mammography.

Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images

Automated image analysis aims to extract relevant information from contrast-enhanced magnetic resonance images (CE-MRI) of the breast and improve the accuracy and consistency of image interpretation. In this work, we extend the traditional 2D gray-level co-occurrence matrix (GLCM) method to investigate a volumetric texture analysis approach and apply it for the characterization of breast MR lesions. Our database of breast MR images was obtained using a T1-weighted 3D spoiled gradient echo sequence and consists of 121 biopsy-proven lesions (77 malignant and 44 benign). A fuzzy c-means clustering (FCM) based method is employed to automatically segment 3D breast lesions on CE-MR images. For each 3D lesion, a nondirectional GLCM is then computed on the first postcontrast frame by summing 13 directional GLCMs. Texture features are extracted from the nondirectional GLCMs and the performance of each texture feature in the task of distinguishing between malignant and benign breast lesions is assessed by receiver operating characteristics (ROC) analysis. Our results show that the classification performance of volumetric texture features is significantly better than that based on 2D analysis. Our investigations of the effects of various of parameters on the diagnostic accuracy provided means for the optimal use of the approach.

Thick slices from tomosynthesis data sets: phantom study for the evaluation of different algorithms

Tomosynthesis is a 3-dimensional mammography technique that generates thin slices separated one to the other by typically 1 mm from source data sets. The relatively high image noise in these thin slices raises the value of 1-cm thick slices computed from the set of reconstructed slices for image interpretation. In an initial evaluation, we investigated the potential of different algorithms for generating thick slices from tomosynthesis source data (maximum intensity projection-MIP; average algorithm-AV, and image generation by means of a new algorithm, so-called softMip). The three postprocessing techniques were evaluated using a homogeneous phantom with one textured slab with a total thickness of about 5 cm in which two 0.5-cm-thick slabs contained objects to simulate microcalcifications, spiculated masses, and round masses. The phantom was examined by tomosynthesis (GE Healthcare). Microcalcifications were simulated by inclusion of calcium particles of four different sizes. The slabs containing the inclusions were examined in two different configurations: adjacent to each other and close to the detector and with the two slabs separated by two 1-cm thick breast equivalent material slabs. The reconstructed tomosynthesis slices were postprocessed using MIP, AV, and softMip to generate 1-cm thick slices with a lower noise level. The three postprocessing algorithms were assessed by calculating the resulting contrast versus background for the simulated microcalcifications and contrast-to-noise ratios (CNR) for the other objects. The CNRs of the simulated round and spiculated masses were most favorable for the thick slices generated with the average algorithm, followed by softMip and MIP. Contrast of the simulated microcalcifications was best for MIP, followed by softMip and average projections. Our results suggest that the additional generation of thick slices may improve the visualization of objects in tomosynthesis. This improvement differs from the different algorithms for microcalcifications, speculated objects, and round masses. SoftMip is a new approach combining features of MIP and average showing image properties in between MIP and AV.

Digital mammography: what do we and what don't we know?

High-quality full-field digital mammography has been available now for several years and is increasingly used for both diagnostic and screening mammography. A number of different detector technologies exist, which all have their specific advantages and disadvantages. Diagnostic accuracy of digital mammography has been shown to be at least equivalent to film-screen mammography in a general screening population. Digital mammography is superior to screen-film mammography in younger women with dense breasts due to its ability to selectively optimize contrast in areas of dense parenchyma. This advantage is especially important in women with a genetic predisposition for breast cancer, where intensified early detection programs may have to start from 25 to 30 years of age. Tailored image processing and computer-aided diagnosis hold the potential to further improve the early detection of breast cancer. However, at present no consensus exists among radiologists on which processing is optimal for digital mammograms. Image processing may also vary significantly among vendors with so far limited interoperability. This review aims to summarize the available information regarding the impact of digital mammography on workflow and breast cancer diagnosis.

Tomosynthesis and contrast-enhanced digital mammography: recent advances in digital mammography

Digital mammography is more and more replacing conventional mammography. Initial concerns about an inferior image quality of digital mammography have been largely overcome and recent studies even show digital mammography to be superior in women with dense breasts, while at the same time reducing radiation exposure. Nevertheless, an important limitation of digital mammography remains: namely, the fact that summation may obscure lesions in dense breast tissue. However, digital mammography offers the option of so-called advanced applications, and two of these, contrast-enhanced mammography and tomosynthesis, are promising candidates for improving the detection of breast lesions otherwise obscured by the summation of dense tissue. Two techniques of contrast-enhanced mammography are available: temporal subtraction of images acquired before and after contrast administration and the so-called dual-energy technique, which means that pairs of low/high-energy images acquired after contrast administration are subtracted. Tomosynthesis on the other hand provides three-dimensional information on the breast. The images are acquired with different angulations of the X-ray tube while the object or detector is static. Various reconstruction algorithms can then be applied to the set of typically nine to 28 source images to reconstruct 1-mm slices with a reduced risk of obscuring pathology. Combinations of both advanced applications have only been investigated in individual experimental studies; more advanced software algorithms and CAD systems are still in their infancy and have only undergone preliminary clinical evaluation.

Evaluation of 11-gauge and 9-gauge vacuum-assisted breast biopsy systems in a breast parenchymal model

RATIONALE AND OBJECTIVES

To compare three commercially available vacuum-assisted breast biopsy systems for tissue yield, length and fragmentation of specimens.

MATERIALS AND METHODS

Specimens were acquired from radiolucent (bacon) and radioopaque (turkey breast) tissue using three different commercially available vacuum-assisted breast biopsy devices. Two systems (system 1 and 2) were equipped with 11 G needles, one system (system 3) with a 9 G needle. As for systems 1 and 2 a second chamber for applying the vacuum is attached to the needle, the external maximum diameter was identical for all three systems. 48 specimens were taken out for each tissue type and for each device. Specimens were measured for total weight, individual length, and number of fragments. Differences between groups were analyzed using analysis of variance (ANOVA) and Student's t-test.

RESULTS

For both tissue types, system 1 and 2 showed similar results, for system 3 tissue weight and length of specimens were larger. Differences in lengths and weight were statistically significant between system 1 and 3 and system 2 and 3 (ANOVA, P < 0.05). Differences between length and weight were statistically significant between tissue 1 and 2 for all devices (t-Test < 0.05).

CONCLUSION

As for system 3 a larger tissue yield was obtained with the same number of specimens compared to systems 1 and 2, it can be assumed that the same diagnostic accuracy as for systems 1 and 2 may be achieved for system 3 with less passes through tissue.

Contrast-to-noise ratios of different elements in digital mammography: evaluation of their potential as new contrast agents

PURPOSE

To determine the contrast-to-noise ratios (CNRs) of different elements at different energies using various anode/filter combinations currently employed in digital mammography. The elements investigated included not only elements already used in conventional contrast agents such as gadolinium and iodine but also other elements to investigate their potential as mammographic contrast agents.

MATERIALS AND METHODS

The CNRs of 20 mmol/L bismuth (Bi), gadolinium (Gd), ytterbium (Yb), dysprosium (Dy), and iodine (I) were determined at different slice thicknesses (0.25, 0.5, and 1 cm) of the element solution with an additional 4-cm Plexiglas in relation to water (to simulate dense glandular tissue), oil, and air. The following anode/filter combinations were used: Mo/Mo in the range of 22-34 kVp, Mo/Rh in the range of 36-40 kVp, Rh/Rh in the range of 42-46 kVp, and Mo/Cu in the range of 47-49 kVp. In the range of 22-46 kVp, the mAs were chosen to achieve a fairly uniform dose range (of 4.38-4.71 mGy). Doses were measured using the PTW DIADOS diagnostic dosimeter. The element solutions were examined with a GE Senographe 2000D.

RESULTS

Bismuth showed the best CNR for all energies investigated and in relation to both water and oil. In the energy range below 46 kVp, bismuth (CNR at 30 kVp/50 mAs and 1/0.5/0.25 cm slice thickness: 9.9/6.1/3.4) was followed by Yb (5.9/3.5/2.0), Dy (5.3/3.2/1.9), Gd (4.2/2.5/1.6), and iodine (2.4/1.8/1.5). Bismuth had the best CNR relative to both water (values given above) and oil (Bi: 20.7/11.2/5; Yb: 16.9/8.6/3.6; Dy: 16.6/8.4/3.5; Gd: 15.21/7.5/3.2; I: 13.8/6.3/3.2). The CNR of Bi was also superior to that of the other elements investigated at high energy in combination with copper filters (eg, CNR at 49 kVp Mo/Cu at slice thicknesses of 1/0.5/0.25 cm, relative to water: 9.6/6.0/4.0) but now followed by iodine (7.9/5.3/3.5), Yb (5.8/4.0/2.9), Dy (5.4/3.7/2.8), and Gd (4.7/3.2/2.7). Iodine was the only element of those investigated whose contrast-to-noise ratio was improved with the use of a copper filter at high energies based on its K-edge (increase in CNR from 2.9 to 7.9 from 40 to 49 kVp at 1-cm slice thickness). Nevertheless, the improved CNR of iodine was below that of Bi at low energies and for Mo/Mo or Mo/Rh filters. The contrast of water/fat tended to decrease slightly at higher energies (CNR of water/air at 42 kVp: 33.9, at 48 kVp: 25.6; CNR of oil/air at 42 kVp: 23.8, at 48 kVp: 21.9).

CONCLUSION

Copper filters and higher energies are useful for visualizing iodine-based contrast agents in contrast-enhanced mammography because they markedly improve the CNR relative to water. This technique further benefits from the fact that the CNR of water and fat relative to air markedly decreases at higher energies and with the use of copper filters. Bismuth was found to have a much better CNR than iodine for all energies investigated including the low energy ranges typically used in mammography. These results suggest that bismuth is a potential candidate for a specific mammographic contrast agent.

[Teleradiology and mammography screening: evaluation of a network with dedicated workstations for reporting]

PURPOSE

Evaluation of a system that supports a workflow for breast cancer screening by mammography. The time of installation, system reliability and workstation operation were evaluated.

MATERIALS AND METHODS

The evaluated system (Image Diagnost, Munich, Germany) contains 2 diagnostic mammography workstations, a centralized server, and 2 Dicom shuttles for exchanging images via a physically existing network structure. Temporary archiving is possible. A mask designed for the needs of mammography screening facilitates assignment of BIRADS categories. The system automatically compares the categories assigned by a first and second reviewer and decides whether a consensus conference should be held. In the event that a conference is needed, the reviews are transmitted to the mammography expert responsible for the screening program and the consensus conference. Images are transferred via ISDN, Germany's National Research and Education Network (in the following DFN) and a central server between 2 sites which are approx. 100 km apart. We evaluated the duration of installation, the reliability of the system, and the usability of the workstation. Since we used curative mammography for evaluating the system, the patient age was noted for comparison.

RESULTS

The system was installed in five days. Once installed, the system functioned without any major breakdowns. Mammography units of 2 manufacturers were able to be connected to the system without difficulty. Mammographies of 151 patients were exchanged between the sites and evaluated by 2 radiologists. 57 % of the patients were in the screening age (50-69 years). 9 exams were classified BIRADS 4a, 2 were 4b and 3 were BIRADS 5. The evaluations were technically perfect in 146/151 cases; hanging protocols had to be altered manually in 6 cases; the window/level had to be manually adjusted in 26/151 cases. Magnification was sufficient in all cases. The system exchanges examinations extremely quickly; up to 100 mammography exams may be evaluated in 1 hr.

CONCLUSION

The system supports the workflow given by the German Mammography Screening program both locally and in a network.

Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.

Mammographie-Screening in Deutschland:Wie, wann und warum?

It is well-known from several large randomized trials that regular mammography screening can reduce breast cancer mortality. While in many countries mammography screening programs have been in existence for quite some time, an organized population-based screening program is only now being implemented in Germany. In this review article, the different elements of a mammography screening program and their effect on the cost-benefit ratio are discussed and the planned German screening program is compared to the international programs.

A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images

RATIONALE AND OBJECTIVES

Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective.

MATERIALS AND METHODS

We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method.

RESULTS

For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method.

CONCLUSION

Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.

Digital Mammography Using Iodine-Based Contrast Media

PURPOSE

We sought to evaluate the potential of dynamic contrast enhancement after the intravenous administration of an iodine-based contrast medium in full-field digital mammography.

MATERIAL AND METHODS

A protocol for image acquisition was established for contrast-enhanced mammography and the mammography unit (Senographe 2000D, GE Healthcare, Buc, France) changed as required. The effect of the protocol parameters on imaging was investigated. Subsequently, 21 patients with 25 suspicious lesions of the breast (10 benign, 1 borderline, and 14 malignant) underwent mammography with administration of an iodine-based contrast medium (Ultravist 370, Schering AG, Berlin, Germany), after approval of ethical committee as well as permission of German federal office for Radiation protection, and informed consent from each patient was obtained. Three sequential digital mammographic images of the respective breast were acquired after administration of the contrast medium at a dose of 1 mL/kg body weight and a flow of 4 mL/s. The postcontrast images were acquired 60, 120, and 180 seconds after administration. Subsequently, the precontrast image was logarithmically subtracted from the postcontrast images. Enhancement of the lesions was measured in absolute terms as well as relative to the enhancement of the glandular tissue. The subtracted images were evaluated for lesion depiction and dynamic contrast enhancement. Lesion-enhancement kinetics were compared with the histologic findings.

RESULTS

All malignant lesions were identified on the contrast-enhanced images of digital mammography. Three of the tumors (2 malignant, 1 benign) were detected only by contrast-enhanced mammography and not by standard mammography. Dynamic enhancement curves of benign and malignant tumors in contrast-enhanced mammography look similar to the curves known from gadolinium-enhanced magnetic resonance imaging. Nevertheless differentiation between malignant and benign tumors based on the enhancement patterns cannot be directly taken over from magnetic resonance imaging, as suggested by our initial results. The results are somewhat better when tumor enhancement relative to surrounding glandular tissue is used instead of absolute enhancement.

CONCLUSION

The results of this preliminary study suggest that contrast-enhanced digital mammography is a potentially useful tool for the detection and the differentiation of benign and malignant breast lesions.