Magnetic Resonance Imaging Mammography Research Articles

Breast density quantification in dual-energy mammography using virtual anthropomorphic phantoms.

Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms. The dual-energy decomposition formalism was used to quantify VBD on simulated dual-energy images of anthropomorphic virtual phantoms with known tissue distributions. We simulated 150 phantoms with volumes ranging from 50 to 709mL and VBD ranging from 15% to 60%. Using these results, we validated a correction for the presence of skin and assessed the method's intrinsic bias and variability. As a proof of concept, the method was applied to 14 sets of clinical dual-energy images, and the resulting breast densities were compared to magnetic resonance imaging (MRI) measurements. Virtual phantom VBD measurements exhibited a strong correlation (Pearson's ) with nominal values. The proposed skin correction eliminated the variability due to breast size and reduced the bias in VBD to a constant value of -2%. Disagreement between clinical VBD measurements using MRI and dual-energy mammography was under 10%, and the difference in the distributions was statistically non-significant. VBD measurements in both modalities had a moderate correlation (Spearman's =0.68). Our results in virtual phantoms indicate that the material decomposition method can produce accurate VBD measurements if the presence of a third material (skin) is considered. The results from our proof of concept showed agreement between MRI and dual-energy mammography VBD. Assessment of VBD using dual-energy images could provide complementary information in dual-energy mammography and tomosynthesis examinations.

Comparison of Amritsar scoring system using quantitative contrast enhanced ultrafast magnetic resonance mammography and diffusion technique with conventional Kaiser scoring system

Background: Magnetic resonance imaging (MRI) mammography has been recommended as a problem solving tool in patients with breast lump for which newer imaging protocols like abbreviated, ultrafast MRI and diffusion MRI are now available. For interpretation lexicons like BIRADS and Kaiser score system are available but there is a scope for improvement of results and need for newer lexicons.Methods: Retrospective study of 175 patients of breast lump who had MRI mammography was done. The lesions were labelled as malignant by Kaiser score system (KSS) and a newer scoring system “Amritsar score system” (AMSS). Final diagnoses was confirmed by histological examination with hormone and Her2neu receptor studies. Statistical analysis was done for correlation, sensitivity, specificity and accuracy along with area under curves and the results compared.Results: Study comprised 32/175 patients with malignant nodules. Mean age of 47.2 (range: 44.2-49.6) years with mean nodule size of 2.2 cm (range 1.8-5.5 cm). ADC and Ktrans, Kep, TTE, MS and IAUC60 showed high correlation with size of malignant nodule. Sensitivity of detection was 87.4%, 87.5%, 88.6%, 71.8% and 80% respectively for ADC, Ktrans, Kep, TTE, MS and IAUC60 while specificities were 94, 88.7, 88.7% 90% and 90% respectively. The sensitivity, specificity and accuracy for KSS and AMSS were 62.5%, 88.9%, 72% and 96.5%, 90% and 94% respectively.Conclusions: AMSS is more accurate than KSS and improves the sensitivity and specificity of cancer detection.  Ktrans and ADC imaging parameters not only show high sensitivity for cancer detection but also have a good correlation with the size and nuclear grade to be used as imaging biomarkers.

Challenges and Changes of the Breast Cancer Screening Paradigm.

Since four decades mammography is used for early breast cancer detection in asymptomatic women and still remains the gold standard imaging modality. However, population screening programs can be personalized and women can be divided into different groups based on risk factors and personal preferences. The availability of new and evolving imaging modalities, for example, digital breast tomosynthesis, dynamic-contrast-enhanced magnetic resonance imaging (MRI), abbreviated MRI protocols, diffusion-weighted MRI, and contrast-enhanced mammography leads to new challenges and perspectives regarding the feasibility and potential harms of breast cancer screening. The aim of this review is to discuss the current guidelines for different risk groups, to analyze the recent published studies about the diagnostic performance of the imaging modalities and to discuss new developments and future perspectives. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 6.

Prediction of Pathologic Complete Response in Breast Cancer Patients Comparing Magnetic Resonance Imaging with Ultrasound in Neoadjuvant Setting.

Some subgroups of breast cancer patients receiving neoadjuvant chemotherapy (NACT) show high rates of pathologic complete response (pCR) in the breast, proposing the possibility of omitting surgery. Prediction of pCR is dependent on accurate imaging methods. This study investigated whether magnetic resonance imaging (MRI) is better than ultrasound (US) in predicting pCR in breast cancer patients receiving NACT. This institutional, retrospective study enrolled breast cancer patients receiving NACT who were examined by either MRI or combined US and mammography before surgery from 2016 to 2019. Imaging findings were compared with pathologic response evaluation of the tumor. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for prediction of pCR were calculated and compared between MRI and US. Among 307 patients, 151 were examined by MRI and 156 by US. In the MRI group, 37 patients (24.5 %) had a pCR compared with 51 patients (32.7 %) in the US group. Radiologic complete response (rCR) was found in 35 patients (23.2 %) in the MRI group and 26 patients (16.7 %) in the US group. In the MRI and US groups, estimates were calculated respectively for sensitivity (87.7 % vs 91.4 %), specificity (56.8 % vs 33.3 %), PPV (86.2 % vs 73.8 %), NPV (60.0 % vs 65.4 %), and accuracy (80.1 % vs 72.4 %). In predicting pCR, MRI was more specific than US, but not sufficiently specific enough to be a valid predictor of pCR for omission of surgery. As an imaging method, MRI should be preferred when future studies investigating prediction of pCR in NACT patients are planned.

Risk Management for BRCA1/BRCA2 mutation carriers without and with breast cancer.

We review the management for unaffected BRCA1/2 mutation carriers and the local management of early stage breast cancer. For unaffected BRCA1/2 mutation carriers, surveillance includes annual magnetic resonance imaging (MRI) and mammogram (MG). Novel imaging modalities, including abbreviated protocol MRI, ultrafast/accelerated MRI, and contrast-enhanced digital mammography are being investigated. Risk reducing mastectomy (RRM) should be considered, and nipple-areolar sparing mastectomy (NSM) is now an option. Additionally, risk reducing salpingo-oophorectomy (RRSO) is strongly recommended as it reduces mortality.In BRCA1/2 mutation carriers with breast cancer, BCT is an appropriate treatment option but to reduce risk of second primary, mastectomy and contralateral risk-reducing mastectomy should be considered.

Abstract P3-02-11: Screening Magnetic Resonance Imaging (MRI) of the breast in women at increased lifetime risk for breast cancer: A retrospective single institution study.

Abstract Background: Multiple factors are associated with an increased lifetime risk of breast cancer, including inheritance of an abnormal BRCA 1/2 gene, history of lobular carcinoma in situ (LCIS) or atypical hyperplasia, family history of breast cancer or previous chest wall radiation. In 2007, the American Cancer Society released updated guidelines for breast cancer screening based on risk stratification. These guidelines added annual MRI screening to mammography for women with greater than or equal to a 20–25% lifetime risk. Breast MRI screening trials have consistently demonstrated a higher sensitivity of MRI for malignancy compared with mammography, with an additional cancer yield from MRI of approximately 3%. The purpose of this study was to evaluate MRI screening outcomes in women with an increased risk for breast cancer evaluated in an established breast subspecialty clinic within the University of Wisconsin (UW) Hospital and Clinics. Methods: Patients (Pts) were included if they were seen by a UW breast center nurse practitioner, medical or surgical oncologist between 1/1/2007–3/1/2011 with a diagnosis code of: family history of breast or ovarian cancer, genetic susceptibility to malignant neoplasm or genetic carrier, Hodgkin's disease, LCIS, or atypical hyperplasia. Pts with a co-existing diagnosis of invasive breast cancer or ductal carcinoma in situ prior to initial encounter were excluded. Demographic information, breast cancer risk factors, estimated lifetime risk of breast cancer and screening recommendations were abstracted from the medical record. Results of subsequent breast imaging examinations (including breast MRI, diagnostic and screening mammography, and image-guided biopsies) were analyzed with the use of the mammography information system (PenRad™). Results: Of 276 women who met the inclusion criteria, 148 underwent at least 1 screening breast MRI. The majority of MRI screened pts were premenopausal (82%) and Caucasian (96.6%) with a mean age of 42.5 (range 20–68) at their initial encounter. Eighty five percent had a first degree relative with breast cancer and 72.3% of pts undergoing MRI screening had a documented lifetime risk of breast cancer of 20% or greater using a validated model. Within this MRI-screened cohort, 18.2% had a known genetic predisposition to breast cancer. Over the time assessed, 307 MRIs were performed in the 148 pts. Biopsy was recommended and performed based on the results of the MRI in 31 of 307 exams (10%). Ten cancers were detected for a positive predictive value based on biopsy performed of 32% and an overall cancer yield of 3.3% (10 of 307 MRI exams). All cancers were stage 0 - II. All pts are currently with no evidence of disease. Conclusion: Breast MRI has a high positive predictive value and cancer yield with an acceptable biopsy rate in a diverse group of high risk women undergoing breast MRI at an academic center outside of a clinical trial. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-02-11.

Computational intelligence in biomedical imaging: multidimensional analysis of spatio-temporal patterns

Technical innovations in radiology, such as advanced cross-sectional imaging methods, have opened up new vistas for the exploration of structure and function of the human body enabling both high spatial and temporal resolution. However, these techniques have led to vast amounts of data whose precise and reliable visual analysis by radiologists requires a considerable amount of human intervention and expertise, thus resulting in a cost factor of substantial economic relevance. Hence, the computer-assisted analysis of biomedical image data has moved into the focus of interest as an issue of high priority research efforts. In this context, innovative approaches to exploratory analysis of huge complex spatio-temporal patterns play a key role to improve computer-assisted signal and image processing in radiology. Examples of such approaches are various unsupervised vector quantization methods or supervised function approximation techniques, such as Generalized Radial-Basis-Functions- (GRBF-) neural networks. Recent developments motivated by concepts of computational intelligence are the `Deformable Feature Map' (DM) as an algorithm for self-organized model adaptation, the `Mutual Connectivity Analysis' (MCA) as an instrument for the analysis of large time-series ensembles and the `Exploratory Observation Machine' (XOM) as a novel general framework for learning by self-organization--three methods that the author has invented and applied to biomedical real-world applications. This contribution covers both conceptual foundations and applications of such methods for pattern recognition and analysis to a wide scope of radiological data sets, such as structural and functional segmentation in Magnetic Resonance Imaging (MRI), ranging from functional MRI for human brain mapping to the monitoring of disease progression in multiple sclerosis by automatic lesion segmentation, as well as novel approaches to image time-series analysis in MRI mammography for breast cancer diagnosis. Current projects related to the modeling of speech production and to genome-wide expression analysis of microarray data in bioinformatics confirm the broad applicability of the presented methods.

Cancer risk management strategies and perceptions of unaffected women 5 years after predictive genetic testing for BRCA1/2 mutations

In a French national cohort of unaffected females carriers/non-carriers of a BRCA1/2 mutation, long-term preventive strategies and breast/ovarian cancer risk perceptions were followed up to 5 years after test result disclosure, using self-administered questionnaires. Response rate was 74%. Carriers (N=101) were younger (average age±SD=37±10) than non-carriers (N=145; 42±12). There were four management strategies that comprised 88% of the decisions made by the unaffected carriers: 50% opted for breast surveillance alone, based on either magnetic resonance imaging (MRI) and other imaging (31%) or mammography alone (19%); 38% opted for either risk reducing salpingo-oophorectomy (RRSO) and breast surveillance, based on MRI and other imaging (28%) or mammography alone (10%). The other three strategies were: risk reducing mastectomy (RRM) and RRSO (5%), RRM alone (2%) and neither RRM/RRSO nor surveillance (6%). The results obtained for various age groups are presented here. Non-carriers often opted for screening despite their low cancer risk. Result disclosure increased carriers' short-term high breast/ovarian cancer risk perceptions (P⩽0.02) and decreased non-carriers' short- and long-term perceptions (P<0.001). During follow-up, high breast cancer risk perceptions increased with time among those who had no RRM and decreased in the opposite case; high ovarian cancer risk perceptions increased further with time among those who had no RRSO and decreased in the opposite case; RRSO did not affect breast cancer risk perceptions. Informed decision-making involves letting women know whether opting for RRSO and breast MRI surveillance is as effective in terms of survival as RRM and RRSO.

MRI versus Ultrasonography and Mammography for Preoperative Assessment of Breast Cancer

Mammography and ultrasonography are traditional for preoperative estimation of breast cancer size; magnetic resonance imaging (MRI) is more recent but not as well studied. We compared ultrasonography, mammography, and MRI for preoperative imaging of primary breast cancer presenting as a mass in patients treated at our center over a 2-year period. Of the 61 breast cancers with all three imaging modalities performed, 52 were infiltrating ductal cancer, 5 were infiltrating lobular cancer, 2 were ductal carcinoma in situ, and 2 were other histologic types. When pathologic size was used to determine the accuracy of imaging assessments, the Pearson correlation coefficient was better for MRI (r = 0.80) than ultrasonography (r = 0.57) or mammography (r = 0.26). Mean tumor size was 2.1 cm by mammography, 1.73 cm by ultrasonography, 2.65 cm by MRI, and 2.76 cm by pathology. MRI-based tumor size was within 1 cm of pathologic size in 44 (72%) tumors, > 1 cm above pathologic size in 6 (10%) tumors, and > 1 cm below pathologic size in 11 (18%) tumors. We conclude that MRI is more accurate than either ultrasonography or mammography for assessment of the size of primary breast cancer presenting as a mass.

Contrast-enhanced CT Evaluation of Clinically and Mammographically Occult Multiple Breast Tumors in Women with Unilateral Early Breast Cancer

Magnetic resonance imaging mammography is performed to determine the extent of lesions and to detect occult lesions, but preoperative diagnosis by breast computed tomography (CT) is less common. We performed a retrospective study of detection of mammographically occult multiple lesions using breast CT. The subjects were 407 female patients (median age: 56 years old; median tumor size: 1.9 cm) with breast cancer who underwent preoperative, contrast-enhanced breast CT. CT detected 73 incidental-enhanced breast nodules (median size: 0.8 cm) in 73 patients that were undetectable by conventional methods. Age, size of the main lesion or laterality of lesions did not differ between patients with and without incidental nodules, but the frequency of mastectomy was significantly higher in those with incidental nodules. Of the 73 incidental nodules, 22 (30%) were in the same quadrant as the main lesion, 26 (36%) were in other quadrants and 25 (34%) were in the opposite breast. On qualitative diagnosis by CT, 48 were suspected to be malignant (66%), 17 benign (23%) and eight non-specific (11%). In histological evaluation of 44 of the 48 nodules suspected to be malignant, 24 were malignant (invasive carcinoma: 22, non-invasive carcinoma: 2); of seven of the 17 nodules suspected to be benign, all were benign; and of three of the eight non-specific nodules, one was non-invasive carcinoma. The discovery rate of clinically and mammographically occult multiple lesions by preoperative breast CT was 6%. We conclude that breast CT is useful for the detection of occult lesions and choice of surgical procedure.

MRI accuracy in residual disease evaluation in breast cancer patients treated with neoadjuvant chemotherapy

To assess the accuracy of magnetic resonance imaging (MRI) in evaluating residual disease after neoadjuvant chemotherapy in patients with large breast cancers. Forty-five women with large breast cancers underwent MRI mammography before and after neoadjuvant chemotherapy (three or six cycles). Dynamic MRI was performed using a 1.5 T unit using three-dimensional FSPGR sequences. For each patient tumour size, tumour volume and dynamic curve were obtained before and after neoadjuvant treatment. Residual tumour sizes obtained using MRI were compared with pathological findings to assess the accuracy of MRI in detecting and in measuring residual tumour. The sensitivity, specificity and accuracy of MRI in detecting residual disease was 90.5, 100, and 91.3%, respectively. The mean of largest diameters measured at histology and at MRI were 26 and 28.2mm, respectively. The tumour size correlation coefficient between MRI and pathology measurements was very high: r(2)=0.9657 (p<0.0001). The interclass correlation coefficient between preoperative imaging measurements and pathological measurements of residual disease was 0.944 (95% CI: 0.906-0.982). The presence and size of residual disease in breast patients treated with neoadjuvant chemotherapy could be accurately evaluated using MRI.

Segmentation and classification of dynamic breast magnetic resonance image data

An application of an unsupervised self-organizing neural network—the minimal free energy vector quantization neural network—is reported for the detection and characterization of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (MRI). This technique enables the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake, characterized by subtle differences of signal amplitude and dynamics, this method provides both a set of prototypical time series and a corresponding set of cluster assignment maps, which further provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. We present three different segmentation methods for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. Starting from the conventional methodology, we proceed by introducing the separate concepts of threshold segmentation and cluster analysis based on the minimal free energy vector quantization neural network, and in the last step by combining those two concepts. The results suggest that the minimal free energy vector quantization neural network has the potential to increase the diagnostic accuracy of MRI mammography by improving sensitivity without reduction of specificity.

Differentiation of Mammographically Suspicious Lesions: Evaluation of Breast Ultrasound, MRI Mammography and Electrical Impedance Scanning as Adjunctive Technologies in Breast Cancer Detection

AIM: Various modalities are used as an adjunct to mammography for differentiation of potentially suspicious breast lesions. Electrical impedance scanning (EIS) is a new technique based upon the principle that cancer cells exhibit altered local dielectric properties and thus show measurably higher conductivity values. The accuracy of differentiation of benign and malignant breast lesions was evaluated to determine whether EIS duplicates or supplements the results obtainable from ultrasound (US) or magnetic resonance imaging (MRI). MATERIALS AND METHODS: One hundred mammographically suspicious lesions were examined using US, MRI and EIS. Definitive histology was acquired through either lesion biopsy or surgical excision. RESULTS: Fifty of 62 malignant lesions were correctly identified using EIS (81% overall sensitivity), 24/38 benign lesions were correctly identified as benign (63% specificity). Negative predictive value and positive predictive value of 67 and 78% were observed, respectively. κ-factor evaluation revealed a value of 0.82 between MRI and EIS and 0.62 between US and EIS. CONCLUSIONS: EIS may be a valuable adjunct for differentiation of suspicious mammographic lesions. Based upon the calculated κ-factor, EIS results supplement US examinations. Artifacts (superficial skin lesions, poor contact, air bubbles) currently result in the high false-positive rate of EIS. Malich, A. et al. (2001). Clinical Radiology56, 278–283.

Magnetic resonance imaging screening in women at genetic risk of breast cancer: imaging and analysis protocol for the UK multicentre study

The imaging and analysis protocol of the UK multicentre study of magnetic resonance imaging (MRI) as a method of screening for breast cancer in women at genetic risk is described. The study will compare the sensitivity and specificity of contrast-enhanced MRI with two-view x-ray mammography. Approximately 500 women below the age of 50 at high genetic risk of breast cancer will be recruited per year for three years, with annual MRI and x-ray mammography continuing for up to 5 years. A symptomatic cohort will be measured in the first year to ensure consistent reporting between centres. The MRI examination comprises a high-sensitivity three-dimensional contrast-enhanced assessment, followed by a high-specificity contrast-enhanced study in equivocal cases. Multiparametric analysis will encompass morphological assessment, the kinetics of contrast agent uptake and determination of quantitative pharmacokinetic parameters. Retrospective analysis will identify the most specific indicators of malignancy. Sensitivity and specificity, together with diagnostic performance, diagnostic impact and therapeutic impact will be assessed with reference to pathology, follow-up and changes in diagnostic certainty and therapeutic decisions. Mammography, lesion localisation, pathology and cytology will be performed in accordance with the UK NHS Breast Screening Programme quality assurance standards. Similar standards of quality assurance will be applied for MR measurements and evaluation.