Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Preoperative breast cancer staging with multi-modality imaging and surgical outcomes

PURPOSE

To compare the rates of mastectomy and re-operation after breast-conserving surgery (BCS) among patients who had different pre-operative multi-modality imaging, hence identifying significant predictors of mastectomy and re-operations within each group.

METHOD

Retrospective study of consecutive patients with primary breast cancer treated January 2010 - December 2016, divided in 3 groups, undergoing pre-operative local staging respectively with conventional imaging modalities only (2D mammography, ultrasound (US)), conventional imaging and tomosynthesis (DBT) and/or MRI. The primary outcome was identification of significant predictors of surgical outcomes, within each group. Study variables examined in univariate analysis were age, lesion dimension, breast density, multifocality, tumor size, histology, and if associated with outcomes they were included in binary logistic regression analysis.

RESULTS

Amongst 1547 patients, patient and tumor characteristics differed across the three groups, as did mastectomy rates which were 18 % (102/562) for 2D + US, 36 % (154/428) for 2D + DBT + US, 45 % (250/557) for 2D+/-DBT + US + MRI(p < 0.001). Variables strongly associated with mastectomy were larger lesions and multifocality (as was multi-modality group). Re-operation rate showed an opposite trend: 12.2 % (56/459) for 2D + US, 8 % (22/272) for 2D + DBT + US, 6.5 % (20/306) for 2D+/-DBT + US + MRI. Re-operation rate for 2D+/-DBT + US + MRI was lower than for 2D + US (p = 0.01) but similar to 2D + DBT + US (p = 0.58). Patients who had 2D + US and re-operations had significantly larger lesions, more underestimation, higher proportion of invasive carcinoma with in-situ component than those who did not require re-operation.

CONCLUSIONS

Patients who had larger tumors and multifocal disease were more frequently staged by adding DBT and/or MRI to conventional imaging (mammography and US) which was associated with more extensive surgical treatment but lower reoperation rates.

Breast cancer prevention in high-risk women

Women at high risk of developing breast cancer are a heterogeneous group of women including those with and without high-risk germline mutation/s. Prevention in these women requires a personalised and multidisciplinary approach. Preventive therapy with selective oestrogen receptor modulators (SERMs) like tamoxifen and aromatase inhibitors (AIs) substantially reduces breast cancer risk well beyond the active treatment period. The importance of benign breast disease as a marker of increased breast cancer risk remains underappreciated, and although the benefit of preventive therapy may be greater in such women, preventive therapy remains underutilised in these and other high-risk women. Bilateral Risk-Reducing Mastectomy (BRRM) reduces the risk of developing breast cancer by 90% in high-risk women such as carriers of BRCA mutations. It also improves breast cancer-specific survival in BRCA1 carriers. Bilateral risk-reducing salpingo-oophorectomy may also reduce risk in premenopausal BRCA2 carriers. Further research to improve risk models, to identify surrogate biomarkers of preventive therapy benefit and to develop newer preventive agents is needed.

Bone and lymph node metastases from occult mammary carcinoma: a case report of carcinoma of unknown primary (CUP) Syndrome

Cancer of unknown provenance is a rare disease, accounting approximately for up to 1% of all breast cancers. A 68-year-old female was admitted to the Medical Oncology Unit of Policlinico Universitario G.Martino

because of diffused bone-involvement, with mixed (osteolytic/osteoblastic) features, which interested almost every skeletal structure of the body (vertebral bodies of the entire column, costal skeleton, sternum, proximal third of both humeri, scapulae, clavicles, pelvis and femurs), suspicious for metastatic disease.

Overview of radiomics in breast cancer diagnosis and prognostication

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.

[Breast cancer: News tools in imaging]

Breast cancer imaging is always improving for the last 20 years in spite of digitalization and computer development. News tools in mammography (Digital Breast Tomosynthesis, Contrast enhanced mammography), sonography (elastography, Automated echography), MRI (Diffusion, abbreviated MRI) and Nuclear medicine has the great potential to be the future of breats imaging. But true revolution will be to use the huge volume of "hidden" imaging data, by Intelligence Artificial process or Biological progress (in genomics, proteiomics) to purpose to our patient a personalized imaging.

[Breast interventional imaging]

A wide diversity of diagnostic or therapeutic percutaneous biopsies exists under sonography, under mammography, under MRI or under CT. This review will detail the different techniques for helping diagnosis or therapeutic management.

Evaluation of single-view contrast-enhanced mammography as novel reading strategy: a non-inferiority feasibility study

BACKGROUND

Guidelines recommend screening of high-risk women using breast magnetic resonance imaging (MRI). Contrast-enhanced mammography (CEM) has matured, providing excellent diagnostic accuracy. To lower total radiation dose, evaluation of single-view (1 V) CEM exams might be considered instead of double-view (2 V) readings as an alternative reading strategy in women who cannot undergo MRI.

METHODS

This retrospective non-inferiority feasibility study evaluates whether the use of 1 V results in an acceptable sensitivity for detecting breast cancer (non-inferiority margin, - 10%). CEM images from May 2013 to December 2017 were included. 1 V readings were performed by consensus opinion of three radiologists, followed by 2 V readings being performed after 6 weeks. Cases were considered "malignant" if the final BI-RADS score was ≥ 4, enabling calculation of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Histopathological results or follow-up served as a gold standard.

RESULTS

A total of 368 cases were evaluated. Mean follow-up for benign or negative cases was 20.9 months. Sensitivity decreased by 9.6% from 92.9 to 83.3% when only 1 V was used for evaluation (p < 0.001). The lower limit of the 90% confidence interval around the difference in sensitivity between 1 V and 2 V readings was - 15% and lies below the predefined non-inferiority margin of - 10%. Hence, non-inferiority of 1 V to 2 V reading cannot be concluded. AUC for 1 V was significantly lower, 0.861 versus 0.899 for 2 V (p = 0.0174).

CONCLUSION

Non-inferiority of 1 V evaluations as an alternative reading strategy to standard 2 V evaluations could not be concluded. 1 V evaluations had lower diagnostic performance compared with 2 V evaluations.

KEY POINTS

• To lower radiation exposure used in contrast-enhanced mammography, we studied a hypothetical alternative strategy: single-view readings (1 V) versus (standard) double-view readings (2 V). • Based on our predefined margin of - 10%, non-inferiority of 1 V could not be concluded. • 1 V evaluation is not recommended as an alternative reading strategy to lower CEM-related radiation exposure.

Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990-0.997); for breast volume, r = 0.985 (95% CI 0.934-0.994); and for FGT volume, r = 0.979 (95% CI 0.958-0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819-0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630-0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual's breast cancer risk stratification.

Mammographic screening in male patients at high risk for breast cancer: is it worth it?

PURPOSE

To investigate the utility of mammography for breast cancer screening in a population of males at increased risk for breast cancer.

METHODS

In this HIPAA-compliant institutional review board-approved single-institution study, mammography records and clinical data of 827 male patients who underwent digital mammography from September 2011-July 2018 were analyzed via the electronic medical record. 664 of these men presented with masses, pain, or nipple discharge and were excluded from this study. The remaining 163 asymptomatic men with familial and/or personal history of breast cancer, or with a known germline mutation in BRCA, underwent screening mammography and were included in this analysis.

RESULTS

163 asymptomatic men (age: mean 63 years, range 24-87 years) underwent 806 screening mammograms. 125/163 (77%) had a personal history of breast cancer and 72/163 (44%) had a family history of breast cancer. 24/163 (15%) were known mutation carriers: 4/24 (17%) BRCA1 and 20/24 (83%) BRCA2. 792/806 (98%) of the screening mammograms were negative (BI-RADS 1 or 2); 10/806 (1.2%) were classified as BI-RADS 3, all of which were eventually downgraded to BI-RADS 2 on follow-up. 4/806 (0.4%) mammograms were abnormal (BI-RADS 4/5): all were malignant. The cancer detection rate in this cohort was 4.9 cancers/1000 examinations.

CONCLUSIONS

In our cohort, screening mammography yielded a cancer detection rate of 4.9 cancers/1000 examinations which is like the detection rate of screening mammography in a population of women at average risk, indicating that screening mammography is of value in male patients at high risk for breast cancer.

Breast cancer

Breast cancer is the most frequent malignancy in women worldwide and is curable in ~70-80% of patients with early-stage, non-metastatic disease. Advanced breast cancer with distant organ metastases is considered incurable with currently available therapies. On the molecular level, breast cancer is a heterogeneous disease; molecular features include activation of human epidermal growth factor receptor 2 (HER2, encoded by ERBB2), activation of hormone receptors (oestrogen receptor and progesterone receptor) and/or BRCA mutations. Treatment strategies differ according to molecular subtype. Management of breast cancer is multidisciplinary; it includes locoregional (surgery and radiation therapy) and systemic therapy approaches. Systemic therapies include endocrine therapy for hormone receptor-positive disease, chemotherapy, anti-HER2 therapy for HER2-positive disease, bone stabilizing agents, poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and, quite recently, immunotherapy. Future therapeutic concepts in breast cancer aim at individualization of therapy as well as at treatment de-escalation and escalation based on tumour biology and early therapy response. Next to further treatment innovations, equal worldwide access to therapeutic advances remains the global challenge in breast cancer care for the future.

Prediction of Treatment Response to Neoadjuvant Chemotherapy for Breast Cancer via Early Changes in Tumor Heterogeneity Captured by DCE-MRI Registration

We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC = 0.78 (±0.03) vs 0.71 (±0.04), p < 0.05 and RFS (C-statistic = 0.76 ( ± 0.05), vs 0.63 ( ± 0.01)), p < 0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p > 0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p < 0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.

Nipple-sparing mastectomy: external validation of a three-dimensional automated method to predict nipple occult tumour involvement on preoperative breast MRI

BACKGROUND

Preoperative evaluation of nipple-areola complex (NAC) tumour involvement is crucial to select patients candidates for nipple-sparing mastectomy. Our aim was to validate a previously developed automated method able to compute the three-dimensional (3D) tumour-to-NAC distance (the most predictive parameter of nipple involvement), using magnetic resonance imaging (MRI) datasets acquired with a scanner and protocol different from those of the development phase.

METHODS

We performed a retrospective analysis of 77 patients submitted to total mastectomy and preoperatively studied with MRI. The new method consisted of automated segmentation of both NAC and tumour and subsequent computation of the 3D distance between them; standard manual two-dimensional segmentation was independently performed. Paraffin-embedded section examination of the removed NAC was performed to identify the neoplastic involvement. The ability of both methods to discriminate between patients with and without NAC involvement was compared using receiver operating characteristic (ROC) analysis.

RESULTS

The 3D tumour-to-NAC distance was correctly computed for 72/77 patients (93.5%); tumour and NAC segmentation method failed in two and three cases, respectively. The diagnostic performance of the 3D automated method at best cut-off values was consistently better than that of the 2D manual method (sensitivity 78.3%, specificity 71.4%, positive predictive value 87.5%, negative predictive value 56.3%, and AUC 0.77 versus 73.9%, 61.2%, 47.2%, 83.3%, and 0.72, respectively), even if the difference did not reach statistical significance (p = 0.431).

CONCLUSIONS

The introduction of the 3D automated method in a clinical setting could improve the diagnostic performance in the preoperative assessment of NAC tumour involvement.

Population-based study of the effect of preoperative breast MRI on the surgical management of ductal carcinoma in situ

BACKGROUND

Determinants of the use of breast MRI in patients with ductal carcinoma in situ (DCIS) in the Netherlands were studied, and whether using MRI influenced the rates of positive resection margins and mastectomies.

METHODS

All women aged less than 75 years, and diagnosed with DCIS between 2011 and 2015, were identified from the Netherlands Cancer Registry. Multivariable logistic regression analyses were performed, adjusting for incidence year, age, hospital type, DCIS grade and multifocality.

RESULTS

Breast MRI was performed in 2382 of 10 415 DCIS cases (22·9 per cent). In multivariable analysis, patients aged less than 50 years, those with high- or intermediate-grade DCIS and patients with multifocal disease were significantly more likely to have preoperative MRI. Patients undergoing MRI were more likely to have a mastectomy, either as first surgical treatment or following breast-conserving surgery (BCS) in the event of positive margins (odds ratio (OR) 2·11, 95 per cent c.i. 1·91 to 2·33). The risk of positive surgical margins after BCS was similar for those with versus without MRI. The secondary mastectomy rate after BCS was higher in patients who had MRI, especially in women aged less than 50 years (OR 1·94, 1·31 to 2·89). All findings were similar for low- and intermediate/high-grade DCIS.

CONCLUSION

Adding MRI to conventional breast imaging did not improve surgical outcome in patients diagnosed with primary DCIS. The likelihood of undergoing a mastectomy was twice as high in the MRI group, and no reduction in the risk of margin involvement was observed after BCS.

Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review

We reviewed technical parameters, acquisition protocols and adverse reactions (ARs) for contrast-enhanced spectral mammography (CESM). A systematic search in databases, including MEDLINE/EMBASE, was performed to extract publication year, country of origin, study design; patients; mammography unit/vendor, radiation dose, low-/high-energy tube voltage; contrast molecule, concentration and dose; injection modality, ARs and acquisition delay; order of views; examination time. Of 120 retrieved articles, 84 were included from 22 countries (September 2003-January 2019), totalling 14012 patients. Design was prospective in 44/84 studies (52%); in 70/84 articles (83%), a General Electric unit with factory-set kVp was used. Per-view average glandular dose, reported in 12/84 studies (14%), ranged 0.43-2.65 mGy. Contrast type/concentration was reported in 79/84 studies (94%), with Iohexol 350 mgI/mL mostly used (25/79, 32%), dose and flow rate in 72/84 (86%), with 1.5 mL/kg dose at 3 mL/s in 62/72 studies (86%). Injection was described in 69/84 articles (82%), automated in 59/69 (85%), manual in 10/69 (15%) and flush in 35/84 (42%), with 10-30 mL dose in 19/35 (54%). An examination time < 10 min was reported in 65/84 studies (77%), 120 s acquisition delay in 65/84 (77%) and order of views in 42/84 (50%) studies, beginning with the craniocaudal view of the non-suspected breast in 7/42 (17%). Thirty ARs were reported by 14/84 (17%) studies (26 mild, 3 moderate, 1 severe non-fatal) with a pooled rate of 0.82% (fixed-effect model). Only half of CESM studies were prospective; factory-set kVp, contrast 1.5 mL/kg at 3 mL/s and 120 s acquisition delay were mostly used; only 1 severe AR was reported. CESM protocol standardisation is advisable.

Breast MRI in the era of diffusion weighted imaging: do we still need signal-intensity time curves?

Objective

Dynamic contrast-enhanced imaging of the initial (IP) and delayed phase (DP) is an integral part of any clinical breast MRI protocol. Furthermore, DWI is increasingly used as an add-on sequence by the breast-imaging community. We investigated whether DWI could be used as a substitute DP.

Material and methods

One hundred thirty-two consecutive patients with equivocal or suspicious findings at ultrasound and/or mammography received a full diagnostic breast MRI according to international recommendations. Histopathological verification served as reference standard. We evaluated three sections of the MRI protocol: IP, DP, and apparent diffusion coefficient (ADC) maps derived from DWI. Circular ROIs (regions of interest, mean size 5–10 mm²) were drawn into the enhancing parts of the lesion (first postcontrast). ROIs were transferred to the corresponding location on ADC maps and IP and DP images. Mean ROI values were investigated signal intensity (SI): (1) Initial-phase enhancement = (SI(IP) − SI(precontrast))/SI(precontrast); (2) Delayed-phase enhancement = (SI(DP) − SI(IP))/SI(IP); (3) ADC. Multiparametric combinations were computed using logistic regression analysis: (1) IP+: Initial-phase enhancement and ADC; (2) Curve: Initial-phase enhancement and delayed-phase enhancement; (3) Curve+: Curve and ADC. The diagnostic performances of these feature combinations to diagnose malignancy were compared by the area under the receiver-operating characteristics curve (AUC).

Results

One hundred thirty-two patients (age: mean = 57.1 years, range 23–83 years) with 145 lesions were included (malignant/benign 101/44). IP+ (AUC = 0.877) outperformed Curve (AUC = 0.788, p = 0.03). Curve+ was not superior to IP+ (p = 1).

Conclusion

DWI could substitute DP. Because DWI is typically used as an add-on to IP and DP, our results might help to abbreviate and to simplify current practice of breast MRI.

Key Points

• DWI provides similar but superior diagnostic information for diagnosis of malignancy in enhancing breast lesions compared to DP.

• Adding DP to DWI does not provide incremental information to distinguish benign from malignant lesions.

• DWI could substitute DP. As DWI is typically used as an add-on to IP and DP, our findings might help to abbreviate and to simplify current breast MRI practice.

Digital Analysis in Breast Imaging

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

Machine learning in breast MRI

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.

Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images

Contrast-Enhanced Spectral Mammography (CESM) is a novelty instrumentation for diagnosing of breast cancer, but it can still be considered operator dependent. In this paper, we proposed a fully automatic system as a diagnostic support tool for the clinicians. For each Region Of Interest (ROI), a features set was extracted from low-energy and recombined images by using different techniques. A Random Forest classifier was trained on a selected subset of significant features by a sequential feature selection algorithm. The proposed Computer-Automated Diagnosis system is tested on 48 ROIs extracted from 53 patients referred to Istituto Tumori “Giovanni Paolo II” of Bari (Italy) from the breast cancer screening phase between March 2017 and June 2018. The present method resulted highly performing in the prediction of benign/malignant ROIs with median values of sensitivity and specificity of 87.5% and 91.7%, respectively. The performance was high compared to the state-of-the-art, even with a moderate/marked level of parenchymal background. Our classification model outperformed the human reader, by increasing the specificity over 8%. Therefore, our system could represent a valid support tool for radiologists for interpreting CESM images, both reducing the false positive rate and limiting biopsies and surgeries.

Fat necrosis in the Breast: A systematic review of clinical

Breast fat necrosis (FN) originates from aseptic fat saponification, which is a typical lipid cyst or a spiculated lesion called mammographic presentation which mimics malignancy. In order to avoid biopsy, it would be necessary to identify the spectrum of fat necrosis appearances. A systematic research was conducted in October 2018 by using PubMed, MEDLINE, Embase, Google Scholar databases and Google to search for science literature published after 2004. Therefore, the aim of this systematic review, it is that the FN can provide radiologists, surgeons, and oncologists with better insight and help them manage the condition efficiently.

Breast Cancer Screening in High-Risk Patients during Pregnancy and Breastfeeding: A Systematic Review of the Literature

There are currently no clear guidelines for high-risk breast cancer screening during the pregnancy and breastfeeding periods. The objective of this systematic review (SR) was to assess the available evidence pertaining to breast cancer screening recommendations in this population with the aim of supporting future guidelines. We performed a SR of the literature using the electronic databases MEDLINE and Embase. Predetermined inclusion and exclusion criteria were used during the abstract screening and full-text data extraction phases. We retrieved 2,274 abstracts after removal of duplicates, from which 16 studies were included based on predetermined eligibility criteria. Most of the studies found were narrative reviews and expert opinions. Clinical breast exam (CBE) was recommended by 12 studies during pregnancy and by 6 studies in the breastfeeding period. Mammography was recommended in the breastfeeding period by 2 studies. Magnetic resonance imaging was recommended in the breastfeeding period by 2 studies. Ultrasound was considered not appropriate for screening in this population. The information extracted from this SR is based primarily on expert opinion and anecdotal evidence, which explains the lack of standardized guidelines for high-risk breast cancer screening in this population. However, expert opinion may be a surrogate outcome for high-risk breast cancer screening recommendations in this subset of patients, and as such, may justify the clinical management to be tailored accordingly. This SR summarizes the evidence pertaining to high-risk breast cancer screening during pregnancy and breastfeeding, which could serve as a catalyst for future research on the topic.

Diffusion-Weighted MRI of Breast Cancer: Improved Lesion Visibility and Image Quality Using Synthetic b-Values

BACKGROUND

Diffusion-weighted imaging (DWI) is an MRI technique with the potential to serve as an unenhanced breast cancer detection tool. Synthetic b-values produce images with high diffusion weighting to suppress residual background signal, while avoiding additional measurement times and reducing artifacts.

PURPOSE

To compare acquired DWI images (at b = 850 s/mm² ) and different synthetic b-values (at b = 1000-2000 s/mm² ) in terms of lesion visibility, image quality, and tumor-to-tissue contrast in patients with malignant breast tumors.

STUDY TYPE

Retrospective.

POPULATION

Fifty-three females with malignant breast lesions.

FIELD STRENGTH/SEQUENCE

T₂ w, DWI EPI with STIR fat-suppression, and dynamic contrast-enhanced T₁ w at 3T.

ASSESSMENT

From acquired images using b-values of 50 and 850 s/mm² , synthetic images were calculated at b = 1000, 1200, 1400, 1600, 1800, and 2000 s/mm² . Four readers independently rated image quality, lesion visibility, preferred b-value, as well as the lowest and highest b-value, over the range of b-values tested, to provide a diagnostic image.

STATISTICAL TESTS

Medians and mean ranks were calculated and compared using the Friedman test and Wilcoxon signed-rank test. Reproducibility was analyzed by intraclass correlation (ICC), Fleiss, and Cohen's κ.

RESULTS

Relative signal-to-noise and contrast-to-noise ratios decreased with increasing b-values, while the signal-intensity ratio between tumor and tissue increased significantly (P < 0.001). Intermediate b-values (1200-1800 s/mm² ) were rated best concerning image quality and lesion visibility; the preferred b-value mostly lay at 1200-1600 s/mm² . Lowest and highest acceptable b-values were 850 s/mm² and 2000 s/mm² . Interreader agreement was moderate to high concerning image quality (ICC: 0.50-0.67) and lesion visibility (0.70-0.93), but poor concerning preferred and acceptable b-values (κ = 0.032-0.446).

DATA CONCLUSION

Synthetically increased b-values may be a way to improve tumor-to-tissue contrast, lesion visibility, and image quality of breast DWI, while avoiding the disadvantages of performing DWI at very high b-values.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1754-1761.

Comparison between opportunistic and organised breast cancer mammography screening in the Swiss canton of Fribourg

BACKGROUND

Breast cancer screening mammography is widespread in industrialised countries within the framework of public health program or opportunist form. Only few data exist on the comparison of effectiveness between organised and opportunistic screening. The aim of this study is to compare organised and opportunistic screening using population-based data from the Fribourg cancer registry, Switzerland.

METHODS

We included all first primary breast adenocarcinoma diagnosed between 2006 and 2014 in women aged 50-69 years resident in the canton of Fribourg. We considered only breast cancer discovered by mammography screening. We compared patients, tumour characteristics and treatment modalities between breast cancer detected by the organised screening program versus opportunistic screening using logistic regression.

RESULTS

Out of 989 patients diagnosed with breast cancer, 402 (40.6%) were diagnosed by organised and 205 (20.7%) by opportunistic screening. Women with breast cancer detected within the screening program were more likely to be from rural areas (P = 0.035) and lived less frequently in high favoured regions (P = 0.020). They presented more frequently in situ than invasive cancer (P = 0.022). For patients with invasive breast cancer, those detected by the program were less likely to undergo mastectomy (P = 0.06) and consequently, they were more likely to undergo radiation therapy (P = 0.003). Adjustment for area of residence and financial context of the region did not modify the results presented.

CONCLUSIONS

The present study reports an increased rate of detection of carcinoma in situ in organised screening program as compared to opportunistic screening mammographies, an indirect evidence of a higher radiologic sensitivity. Furthermore, the results show a trend towards more mastectomies among patients with breast cancer discovered after opportunistic than after organized mammography screening, reflecting lower treatment burden. Those results were independent of socio-economic factors which differed across screening groups.

Artificial intelligence in breast imaging

This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging. Traditional CAD systems in mammography screening have followed a rules-based approach, incorporating domain knowledge into hand-crafted features before using classical machine learning techniques as a classifier. The first commercial CAD system, ImageChecker M1000, relies on computer vision techniques for pattern recognition. Unfortunately, CAD systems have been shown to adversely affect some radiologists' performance and increase recall rates. The Digital Mammography DREAM Challenge was a multidisciplinary collaboration that provided 640,000 mammography images for teams to help decrease false-positive rates in breast cancer screening. Winning solutions leveraged deep learning's (DL) automatic hierarchical feature learning capabilities and used convolutional neural networks. Start-ups Therapixel and Kheiron Medical Technologies are using DL for breast cancer screening. With increasing use of digital breast tomosynthesis, specific artificial intelligence (AI)-CAD systems are emerging to include iCAD's PowerLook Tomo Detection and ScreenPoint Medical's Transpara. Other AI-CAD systems are focusing on breast diagnostic techniques such as ultrasound and magnetic resonance imaging (MRI). There is a gap in the market for contrast-enhanced spectral mammography AI-CAD tools. Clinical implementation of AI-CAD tools requires testing in scenarios mimicking real life to prove its usefulness in the clinical environment. This requires a large and representative dataset for testing and assessment of the reader's interaction with the tools. A cost-effectiveness assessment should be undertaken, with a large feasibility study carried out to ensure there are no unintended consequences. AI-CAD systems should incorporate explainable AI in accordance with the European Union General Data Protection Regulation (GDPR).

Local Tumor Staging of Breast Cancer: Digital Mammography versus Digital Mammography Plus Tomosynthesis

Background Combined digital mammography (DM) and digital breast tomosynthesis (DBT) (hereafter, DM plus DBT) has increased cancer detection rates when compared with those achieved with DM-only screening. However, there is limited literature on DBT as an adjunct to mammography in the staging of known breast cancers. Purpose To compare the diagnostic accuracy of DM alone with that of DM plus DBT in the identification of additional ipsilateral and contralateral lesions in women with newly diagnosed breast cancer. Materials and Methods This prospective study ( https://clinicaltrials.gov , NCT01881880) included 166 women with breast cancer (mean age, 59.5 years ± 11; age range, 40-87 years) and used the aforementioned techniques, with breast MRI and pathologic verification of all suspected lesions as the reference standards. Four radiologists independently reviewed the DM and DM plus DBT images using the American College of Radiology Breast Imaging Reporting and Data Systems criteria for diagnosis of index lesions and presence of additional disease. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) obtained for DM and DM plus DBT were compared by using the McNemar test. Results Twenty-four women (14%) exhibited multifocal lesions; 20 (12%), multicentric lesions; 39 (23%), additional ipsilateral lesions; and 18 (11%), bilateral lesions. The sensitivities were higher for DM plus DBT than for DM in the diagnosis of multicentric (51% [41 of 80] vs 37% [30 of 80], P = .002) and additional ipsilateral (52% [81 of 156] vs 44% [69 of 156], P = .007) lesions. The AUC was larger for DM plus DBT than for DM (0.74 vs 0.67, P = .02) in the diagnosis of bilateral breast cancer. No significant differences in specificity were noted. The added diagnostic value of DBT was limited to the group of women with nondense breasts: For diagnosis of ipsilateral lesions, AUC of DM plus DBT versus DM was 0.74 versus 0.70 (P = .04). For diagnosis of contralateral lesions, AUC of DM plus DBT verus DM was 0.76 versus 0.68 (P = .02). Conclusion The combination of digital mammography with digital breast tomosynthesis improves diagnostic accuracy for additional ipsilateral and contralateral breast cancer in women with nondense breasts. © RSNA, 2019 See also the editorial by Moy in this issue.

Surgical attitudes toward preoperative breast magnetic resonance imaging in women with early-stage breast cancer

Background

Preoperative breast magnetic resonance imaging (mri) is commonly requested by surgeons in the initial workup of women with breast cancer; however, its use is controversial. We performed a survey of breast cancer surgeons across Canada to investigate current knowledge about, attitudes to, and self-reported use of preoperative breast mri in a publicly funded health care system in light of the limited evidence to support it.

Methods

All identified general surgeons in Canada were mailed a survey instrument designed to probe current practice and knowledge of published trials.

Results

Of 403 responding surgeons, 233 (58%) indicated that they performed breast cancer surgery. Of those 233, 218 (94%) had access to breast mri and completed the entire survey. Overall, 54.6% of responding surgeons felt that breast mri was useful in surgical planning, and more than half (58.3%) indicated that their frequency of use was likely to increase over the next 5 years. Surgeons found preoperative mri most useful in detecting mammographically occult disease (71.5% of respondents) and in planning for breast-conserving surgery (57.3%). The main limitations reported were timely access to mri (51%) and false positives (36.7%). Responses suggest a knowledge gap in awareness of published trials in breast mri.

Conclusions

Our study found that, in early-stage breast cancer, self-reported use of mri by breast cancer surgeons in Canada varied widely. Reported indications did not align with published data, and significant gaps in self-reported knowledge of the data were evident. Our results would support the development and dissemination of guidelines to optimize use of mri.

Proton MR spectroscopy in the breast: Technical innovations and clinical applications

Proton magnetic resonance spectroscopy (MRS) is a promising noninvasive diagnostic technique for investigation of breast cancer metabolism. Spectroscopic imaging data may be obtained following contrast-enhanced MRI by applying the point-resolved spectroscopy sequence (PRESS) or the stimulated echo acquisition mode (STEAM) sequence from the MR voxel encompassing the breast lesion. Total choline signal (tCho) measured in vivo using either a qualitative or quantitative approach has been used as a diagnostic test in the workup of malignant breast lesions. In addition to tCho metabolites, other relevant metabolites, including multiple lipids, can be detected and monitored. MRS has been heavily investigated as an adjunct to morphologic and dynamic MRI to improve diagnostic accuracy in breast cancer, obviating unnecessary benign biopsies. Besides its use in the staging of breast cancer, other promising applications have been recently investigated, including the assessment of treatment response and therapy monitoring. This review provides guidance on spectroscopic acquisition and quantification methods and highlights current and evolving clinical applications of proton MRS. Level of Evidence 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Discrimination between breast invasive ductal carcinomas and benign lesions by optimizing quantitative parameters derived from dynamic contrast-enhanced MRI using a semi-automatic method

BACKGROUND

To propose a semi-automatic method for distinguishing invasive ductal carcinomas from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

142 cases were included. In the conventional method, the region of interest for a breast lesion was drawn manually and the corresponding mean time-signal intensity curve (TIC) was qualitatively categorized. Only one quantitative parameter was obtained: the maximum slope of increase (MSI). By contrast, the proposed method extracted the suspicious breast lesion semi-automatically. Besides MSI, more quantitative parameters reflecting perfusion information were derived from the mean TIC and lesion region, including the signal intensity slope (SI_slope), initial percentage of enhancement, percentage of peak enhancement, early signal enhancement ratio, and second enhancement percentage. The mean TIC was categorized quantitatively according to the value of SI_slope. Regression models were established. The diagnostic performance differed between the new and conventional methods according to the Wilcoxon rank-sum test and receiver operating characteristic analysis.

RESULTS

According to the TIC categorization results, the accuracies of the traditional and the new method were 59.16% and 76.05%, respectively (P < 0.05). The accuracy was 63.35% for MSI, which was derived from the manual method. For the semi-automatic method, the accuracies were 81.0% and 78.9% for the lesion region and the corresponding mean TIC regression models, respectively.

CONCLUSIONS

The results demonstrate that our proposed semi-automatic method is beneficial for discriminating breast IDCs and benign lesions based on DCE-MRI, and this method should be considered as a supplementary tool for subjective diagnosis by clinical radiologists.

Potential of Noncontrast Magnetic Resonance Imaging With Diffusion-Weighted Imaging in Characterization of Breast Lesions: Intraindividual Comparison With Dynamic Contrast-Enhanced Magnetic Resonance Imaging

OBJECTIVES

The aim of this study was to assess the potential of noncontrast magnetic resonance imaging (NC-MRI) with diffusion-weighted imaging (DWI) in characterization of breast lesions in comparison to dynamic contrast-enhanced MRI (DCE-MRI) at 3 T.

MATERIALS AND METHODS

Consecutive patients with conventional imaging (mammography, ultrasound) BI-RADS 4/5 findings were included in this institutional review board-approved single-center study. All underwent 3 T breast MRI including readout-segmented DWI, DCE, and T2-weighted sequences. Final diagnosis was defined by histopathology or follow-up (>24 months). Two experienced radiologists (R1, R2) independently assigned lesion conspicuity (0 = minimal to 3 = excellent) and BI-RADS scores to NC-MRI (readout-segmented DWI including apparent diffusion coefficient maps) and DCE-MRI (DCE and T2-weighted). Receiver operating characteristics, κ statistics, and visual grading characteristics analysis were applied.

RESULTS

Sixty-seven malignant and 56 benign lesions were identified in 113 patients (mean age, 54 ± 14 years). Areas under the receiver operating characteristics curves were similar: DCE-MRI: 0.901 (R1), 0.905 (R2); NC-MRI: 0.882 (R1), 0.854 (R2); P > 0.05, respectively. The κ agreement was 0.968 (DCE-MRI) and 0.893 (NC-MRI). Visual grading characteristics analysis revealed superior lesion conspicuity by DCE-MRI (0.661, P < 0.001).

CONCLUSIONS

Diagnostic performance and interreader agreement of both NC-MRI and DCE-MRI is high, indicating a potential use of NC-MRI as an alternative to DCE-MRI. However, inferior lesion conspicuity and lower interreader agreement of NC-MRI need to be considered.

Reducing the dose of gadolinium-based contrast agents for DCE-MRI guided SBRT: The effects on inter and intra observer variability for preoperative target volume delineation in early stage breast cancer patients

BACKGROUND AND PURPOSE

This study aimed to determine the effects of reducing the dose of contrast agent (CA) in a DCE-MRI scan on inter- and intra-observer variability in the context of MRI-guided target volume delineation for stereotactic body radiation therapy of early stage breast cancer patients. This is in hopes of reducing risks to patients due to findings of residual CA in brain and bone.

MATERIALS AND METHODS

Twenty-three patients receiving neoadjuvant radiation therapy were enrolled. Five observers delineated the gross target volume (GTV) using DCE-MRI for guidance. 14/23 patients received the full clinical dose of CA and 9/23 received half. Clinical target volumes (CTV) were created through a 0.5 cm uniform expansion. Several metrics were used to quantify the inter and intra-observer reliability including differences in delineation volume and the reliability coefficient.

RESULTS

There were no significant differences in the volume, though half contrast patients had a lower median for both the GTV and CTV (difference of 0.26 cm³ and 1.27 cm³, respectively). All indicated a high degree of agreement between and within observers for both dose groups. However, the full dose group had a greater inter-observer variability, most likely due to the full CA causing more pronounced enhancement in the periphery.

CONCLUSIONS

Reducing the dose of contrast agent did not significantly alter inter- or intra-observer variability. These results have prompted our centre to reduce the dose of gadolinium in all patients enrolled in the SIGNAL trial.

Problem-solving breast MRI: useful or a source of new problems?

PURPOSE

We aimed to evaluate the findings and results from breast magnetic resonance imaging (MRI) examinations performed for problem-solving purposes due to inconclusive conventional imaging findings.

METHODS

Imaging findings, biopsy and follow-up results were retrospectively evaluated for breast MRI performed for problem-solving purposes at our department between January 2011 and December 2016 for cases whose mammography, tomosynthesis, or ultrasonography findings were inconclusive.

RESULTS

Lesions were identified in 414 of 986 problem-solving MRI examinations, and 13.3% of these lesions were diagnosed as malignant. A total of 124 lesions were additionally found by MRI, and 9.7% of these lesions were diagnosed as malignant. MRI produced false-negative results in four cases. In cases whose conventional imaging methods yielded indefinite results, the sensitivity, specificity, negative and positive predictive values of MRI were found to be 96.3%, 83%, 99.3%, and 46.5%, respectively. For the additional lesions identified, the sensitivity, specificity, negative and positive predictive values of MRI were found to be 91.7%, 69%, 98.7%, and 24%, respectively.

CONCLUSION

Breast MRI is a reliable problem-solving method for excluding malignancy that cannot be confirmed by conventional imaging. In such cases, additional findings from MRI may help identify new cancers that cannot be detected with conventional methods. However, it has moderately low specificity which may cause unnecessary biopsies, follow-ups, and anxiety to patients.

Artificial Intelligence for Breast MRI in 2008-2018: A Systematic Mapping Review

OBJECTIVE

The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI.

MATERIALS AND METHODS

In June 2018, a systematic search of the literature was performed to identify articles on the use of AI in breast MRI. For each article identified, the surname of the first author, year of publication, journal of publication, Web of Science Core Collection journal category, country of affiliation of the first author, study design, dataset, study aim(s), AI methods used, and, when available, diagnostic performance were recorded.

RESULTS

Sixty-seven studies, 58 (87%) of which had a retrospective design, were analyzed. When journal categories were considered, 36% of articles were identified as being included in the radiology and imaging journal category. Contrast-enhanced sequences were used for most AI applications (n = 50; 75%) and, on occasion, were combined with other MRI sequences (n = 8; 12%). Four main clinical aims were addressed: breast lesion classification (n = 36; 54%), image processing (n = 14; 21%), prognostic imaging (n = 9; 13%), and response to neoadjuvant therapy (n = 8; 12%). Artificial neural networks, support vector machines, and clustering were the most frequently used algorithms, accounting for 66%. The performance achieved and the most frequently used techniques were then analyzed according to specific clinical aims. Supervised learning algorithms were primarily used for lesion characterization, with the AUC value from ROC analysis ranging from 0.74 to 0.98 (median, 0.87) and with that from prognostic imaging ranging from 0.62 to 0.88 (median, 0.80), whereas unsupervised learning was mainly used for image processing purposes.

CONCLUSION

Interest in the application of advanced AI methods to breast MRI is growing worldwide. Although this growth is encouraging, the current performance of AI applications in breast MRI means that such applications are still far from being incorporated into clinical practice.

SEOM clinical guidelines in early stage breast cancer (2018)

Breast cancer is the most common cancer in women in our country and it is usually diagnosed in the early and potentially curable stages. Nevertheless, around 20-30% of patients will relapse despite appropriate locoregional and systemic therapies. A better knowledge of this disease is improving our ability to select the most appropriate therapy for each patient with a recent diagnosis of an early stage breast cancer, minimizing unnecessary toxicities and improving long-term efficacy.

Motion artifacts, lesion type, and parenchymal enhancement in breast MRI: what does really influence diagnostic accuracy?

BACKGROUND

Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates.

PURPOSE

To evaluate factors that potentially influence readers' diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type.

MATERIAL AND METHODS

This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy.

RESULTS

Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P < 0.001), and small lesions ≤ 10 mm ( P < 0.021). No further factors (e.g. BPE, FGT) significantly influenced diagnostic estimates. At multivariate analysis, lesion type and size were retained as independent factors influencing the diagnostic performance ( P < 0.033).

CONCLUSION

Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

Breast lesion detection and characterization with contrast-enhanced magnetic resonance imaging: Prospective randomized intraindividual comparison of gadoterate meglumine (0.15 mmol/kg) and gadobenate dimeglumine (0.075 mmol/kg) at 3T

Background

Contrast‐enhanced magnetic resonance imaging (CE‐MRI) of the breast is highly sensitive for breast cancer detection. Multichannel coils and 3T scanners can increase signal, spatial, and temporal resolution. In addition, the T₁‐reduction effect of a gadolinium‐based contrast agent (GBCA) is higher at 3T. Thus, it might be possible to reduce the dose of GBCA at 3T without losing diagnostic information.

Purpose

To compare a three‐quarter (0.075 mmol/kg) dose of the high‐relaxivity GBCA gadobenate dimeglumine, with a 1.5‐fold higher than on‐label dose (0.15 mmol/kg) of gadoterate meglumine for breast lesion detection and characterization at 3T CE‐MRI.

Study Type

Prospective, randomized, intraindividual comparative study.

Population

Eligible were patients with imaging abnormalities (BI‐RADS 0, 4, 5) on conventional imaging. Each patient underwent two examinations, 24–72 hours apart, one with 0.075 mmol/kg gadobenate and the other with 0.15 mmol/kg gadoterate administered in a randomized order. In all, 109 patients were prospectively recruited.

Field Strength/Sequence

3T MRI with a standard breast protocol (dynamic‐CE, T₂w‐TSE, STIR‐T₂w, DWI).

Assessment

Histopathology was the standard of reference. Three blinded, off‐site breast radiologists evaluated the examinations using the BI‐RADS lexicon.

Statistical Tests

Lesion detection, sensitivity, specificity, and diagnostic accuracy were calculated per‐lesion and per‐region, and compared by univariate and multivariate analysis (Generalized Estimating Equations, GEE).

Results

Five patients were excluded, leaving 104 women with 142 histologically verified breast lesions (109 malignant, 33 benign) available for evaluation. Lesion detection with gadobenate (84.5‐88.7%) was not inferior to gadoterate (84.5–90.8%) (P ≥ 0.165). At per‐region analysis, gadobenate demonstrated higher specificity (96.4–98.7% vs. 92.6–97.3%, P ≤ 0.007) and accuracy (96.3–97.8% vs. 93.6–96.1%, P ≤ 0.001) compared with gadoterate. Multivariate analysis demonstrated superior, reader‐independent diagnostic accuracy with gadobenate (odds ratio = 1.7, P < 0.001 using GEE).

Data Conclusion

A 0.075 mmol/kg dose of the high‐relaxivity contrast agent gadobenate was not inferior to a 0.15 mmol/kg dose of gadoterate for breast lesion detection. Gadobenate allowed increased specificity and accuracy.

Level of Evidence: 1

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:1157–1165.