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PURPOSE: To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of dynamic contrast enhanced (DCE)-MRI. METHODS: A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n=25) and benign (n=28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT: The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with AIF showed an AUC between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION: This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Purpose In this study, we compare readout-segmented echo-planar imaging (rs-EPI) Diffusion Weighted Imaging (DWI) to a work-in-progress single-shot EPI with modified Inversion Recovery Background Suppression (ss-EPI-mIRBS) sequence at 3 T using a b-value of 2000 s/mm2 on image quality, lesion visibility and evaluation time. Method From September 2017 to December 2018, 23 women (one case used for training) with known breast cancer were included in this study, after providing signed informed consent. Women were scanned with the conventional rs-EPI sequence and the work-in-progress ss-EPI-mIRBS during the same examination. Four breast radiologists (4–13 years of experience) independently scored both series for overall image quality (1: extremely poor to 9: excellent). All lesions (47 in total, 36 malignant, and 11 benign and high-risk) were evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible) and probability of malignancy (BI-RADS 1 to 5). ADC values were determined by measuring signal intensity in the lesions using dynamic contrast-enhanced (DCE) images for reference. Evaluation times for all assessments were automatically recorded. Results were analyzed using the visual grading characteristics (VGC) and the resulting area under the curve (AUCVGC) method. Statistical analysis was performed in SPSS, with McNemar tests, and paired t-tests used for comparison. Results No significant differences were detected between the two sequences in image quality (AUCVGC: 0.398, p = 0.087) and lesion visibility (AUCVGC: 0.534, p = 0.336) scores. Lesion characteristics (e.g benign and high-risk, versus malignant; small (≤10 mm) vs. larger (>10 mm)) did not result in different image quality or lesion visibility between sequences. Sensitivity (rs-EPI: 72.2% vs. ss-EPImIRBS: 78.5%, p = 0.108) and specificity (70.5% vs. 56.8%, p = 0.210, respectively) were comparable. In both sequences the mean ADC value was higher for benign and high-risk lesions than for malignant lesions (ss-EPI-mIRBS: p = 0.022 and rs-EPI: p = 0.055). On average, ss-EPI-mIRBS resulted in decreased overall reading time by 7.7 s/case (p = 0.067); a reduction of 17%. For malignant lesions, average reading time was significantly shorter using ss-EPI-mIRBS compared to rs-EPI (64.0 s/lesion vs. 75.9 s/lesion, respectively, p = 0.039). Conclusion Based on this study, the ss-EPI sequence using a b-value of 2000 s/mm2 enables for a mIRBS acquisition with quality and lesion conspicuity that is comparable to conventional rs-EPI, but with a decreased reading time.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set ( n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference ( P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists’ performance improved when their predictions were averaged with DL’s predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.©RSNA, 2021.

Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women.

Breast MR imaging is the most sensitive imaging method for the detection of breast cancer and detects more aggressive malignancies than mammography and ultrasound examination. Despite these advantages, breast MR imaging has low use rates for breast cancer screening. Abbreviated breast MR imaging, in which a limited number of breast imaging sequences are obtained, has been proposed as a way to solve cost and patient tolerance issues while preserving the high cancer detection rate of breast MR imaging. This review discusses abbreviated breast MR imaging, including protocols, multicenter clinical trial results, clinical workflow implementation challenges, and future directions.

The aim of this study was to quantify the initial decline and subsequent rebound in breast cancer screening metrics throughout the coronavirus disease 2019 (COVID-19) pandemic.Screening and diagnostic mammographic examinations, biopsies performed, and cancer diagnoses were extracted from the ACR National Mammography Database from March 1, 2019, through May 31, 2021. Patient (race and age) and facility (regional location, community type, and facility type) demographics were collected. Three time periods were used for analysis: pre-COVID-19 (March 1, 2019, to May 31, 2019), peak COVID-19 (March 1, 2020, to May 31, 2020), and COVID-19 recovery (March 1, 2021, to May 31, 2021). Analysis was performed at the facility level and overall between time periods.In total, 5,633,783 screening mammographic studies, 1,282,374 diagnostic mammographic studies, 231,390 biopsies, and 69,657 cancer diagnoses were analyzed. All peak COVID-19 metrics were less than pre-COVID-19 volumes: 36.3% of pre-COVID-19 for screening mammography, 57.9% for diagnostic mammography, 47.3% for biopsies, and 48.7% for cancer diagnoses. There was some rebound during COVID-19 recovery as a percentage of pre-COVID-19 volumes: 85.3% of pre-COVID-19 for screening mammography, 97.8% for diagnostic mammography, 91.5% for biopsies, and 92.0% for cancer diagnoses. Across various metrics, there was a disproportionate negative impact on older women, Asian women, facilities in the Northeast, and facilities affiliated with academic medical centers.COVID-19 had the greatest impact on screening mammography volumes, which have not returned to pre-COVID-19 levels. Cancer diagnoses declined significantly in the acute phase and have not fully rebounded, emphasizing the need to increase outreach efforts directed at specific patient population and facility types.

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.

Rationale and Objectives Pathologic complete response (pCR) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer after HER2-targeted therapy correlates increased disease-free survival and decreased mastectomy rates. The aim of this study was to explore tumor shrinkage patterns and initial tumor enhancement with pCR in HER2-positive breast cancer. Materials and Methods This was an institutional review board-approved retrospective analysis of 51 HER2 positive breast cancer patients with breast MRI both pre- and post-HER2-targeted therapy. Initial enhancement ratio (IER, initial enhancement percentage over baseline at first postcontrast imaging), pattern of tumor shrinkage, and Dynamic contrast enhanced (DCE)-MRI imaging features were assessed. Wilcoxon rank, Spearman correlation, Fisher's exact, and Mann-Whitney tests were used to correlate MRI imaging features with pCR. IER reader agreement was evaluated by intraclass correlation. Binary logistic regression was used to evaluate multivariate associations with pCR. Results 56.9% (29/51) of patients had pCR at surgery. Concentric tumor shrinkage pattern was associated with pCR (p = 0.001, Area under the curve (AUC) 0.778): accuracy 80.4%, specificity 96.6%, and sensitivity of 59.1%. There was no association with pCR and imaging response as defined by RECIST criteria (p = 0.169), pretreatment IER (Reader 1 (R1) p = 0.665, Reader 2 (R2) p = 0.766), or lesion size (p = 0.69). IER was associated with axillary metastases (R1 p = 0.016, R2 Conclusion The shrinkage pattern of HER2-positive tumors after targeted therapy may be associated with pCR. There was no association between IER and pCR. Future studies evaluating the correlation of shrinkage patterns to texture radiomics are of interest.

Current guidelines recommend sentinel lymph node biopsy (SLNB) for patients undergoing mastectomy for a preoperative diagnosis of ductal carcinoma in situ (DCIS). We examined the factors associated with sentinel lymph node positivity for patients undergoing mastectomy for a diagnosis of DCIS on preoperative core biopsy (PCB). The Institutional Breast Cancer Database was queried for patients with PCB demonstrating pure DCIS followed by mastectomy and SLNB from 2010 to 2018. Patients were divided according to final pathology (DCIS or invasive cancer). Clinico-pathologic variables were analyzed using Pearson's chi-squared, Wilcoxon Rank-Sum and logistic regression. Of 3145 patients, 168(5%) had pure DCIS on PCB and underwent mastectomy with SLNB. On final mastectomy pathology, 120(71%) patients had DCIS with 0 positive sentinel lymph nodes (PSLNs) and 48(29%) patients had invasive carcinoma with 5(10%) cases of ≥1 PSLNs. Factors positively associated with upstaging to invasive cancer in univariate analysis included age (P = .0289), palpability (P < .0001), extent of disease on imaging (P = .0121), mass on preoperative imaging (P = .0003), multifocality (P = .0231) and multicentricity (P = .0395). In multivariate analysis, palpability (P = .0080), extent of disease on imaging (P = .0074) and mass on preoperative imaging (P = .0245) remained significant (Table 2). In a subset of patients undergoing mastectomy for DCIS with limited disease on preoperative evaluation, SLNB may be omitted as the risk of upstaging is low. However, patients who present with clinical findings of palpability, large extent of disease on imaging and mass on preoperative imaging have a meaningful risk of upstaging to invasive cancer, and SLNB remains important for management.

As the proportion of women diagnosed with early stage breast cancer increases, the role of imaging for staging and surveillance purposes is considered. National and international guidelines discourage the use of staging imaging for asymptomatic patients newly diagnosed with stage 0 to II breast cancer, even if there is nodal involvement, as unnecessary imaging can delay care and affect outcomes. In asymptomatic patients with a history of stage I breast cancer that received treatment for curative intent, there is no role for imaging to screen for distant recurrences. However, routine surveillance with an annual mammogram is the only imaging test that should be performed to detect an in-breast recurrence or a new primary breast cancer in women with a history of stage I breast cancer. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Abbreviated breast MRI is an emerging technique that is being incorporated into clinical practice for breast cancer imaging and screening. Conventional breast MRI includes barriers such as high examination cost and lengthy examination times which make its use in the screening setting challenging. Abbreviated MRI aims to address these pitfalls by reducing overall examination time and increasing accessibility to MRI while preserving diagnostic accuracy. Sequences selected for abbreviated MRI protocols allow for preserved accuracy in breast cancer detection and characterization. Novel techniques such as ultrafast imaging are being used to provide kinetic information from early post-contrast imaging.

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false-positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography examinations from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.

Background Factors affecting radiologists' performance in screening mammography interpretation remain poorly understood. Purpose To identify radiologists characteristics that affect screening mammography interpretation performance. Materials and Methods This retrospective study included 1223 radiologists in the National Mammography Database (NMD) from 2008 to 2019 who could be linked to Centers for Medicare & Medicaid Services (CMS) datasets. NMD screening performance metrics were extracted. Acceptable ranges were defined as follows: recall rate (RR) between 5% and 12%; cancer detection rate (CDR) of at least 2.5 per 1000 screening examinations; positive predictive value of recall (PPV1) between 3% and 8%; positive predictive value of biopsies recommended (PPV2) between 20% and 40%; positive predictive value of biopsies performed (PPV3) between the 25th and 75th percentile of study sample; invasive CDR of at least the 25th percentile of the study sample; and percentage of ductal carcinoma in situ (DCIS) of at least the 25th percentile of the study sample. Radiologist characteristics extracted from CMS datasets included demographics, subspecialization, and clinical practice patterns. Multivariable stepwise logistic regression models were performed to identify characteristics independently associated with acceptable performance for the seven metrics. The most influential characteristics were defined as those independently associated with the majority of the metrics (at least four). Results Relative to radiologists practicing in the Northeast, those in the Midwest were more likely to achieve acceptable RR, PPV1, PPV2, and CDR (odds ratio [OR], 1.4-2.5); those practicing in the West were more likely to achieve acceptable RR, PPV2, and PPV3 (OR, 1.7-2.1) but less likely to achieve acceptable invasive CDR (OR, 0.6). Relative to general radiologists, breast imagers were more likely to achieve acceptable PPV1, invasive CDR, percentage DCIS, and CDR (OR, 1.4-4.4). Those performing diagnostic mammography were more likely to achieve acceptable PPV1, PPV2, PPV3, invasive CDR, and CDR (OR, 1.9-2.9). Those performing breast US were less likely to achieve acceptable PPV1, PPV2, percentage DCIS, and CDR (OR, 0.5-0.7). Conclusion The geographic location of the radiology practice, subspecialization in breast imaging, and performance of diagnostic mammography are associated with better screening mammography performance; performance of breast US is associated with lower performance. ©RSNA, 2021 Online supplemental material is available for this article.

BACKGROUND: Since 2007, digital mammography and digital breast tomosynthesis (DBT) replaced screen-film mammography. Whether these technologic advances have improved diagnostic performance has, to the knowledge of the authors, not yet been established. PURPOSE: To evaluate the performance and outcomes of surveillance mammography (digital mammography and DBT) performed from 2007 to 2016 in women with a personal history of breast cancer and compare with data from 1996 to 2007 and the performance of digital mammography screening benchmarks. MATERIALS AND METHODS: In this observational cohort study, five Breast Cancer Surveillance Consortium registries provided prospectively collected mammography data linked with tumor registry and pathologic outcomes. This study identified asymptomatic women with American Joint Committee on Cancer anatomic stages 0–III primary breast cancer who underwent surveillance mammography from 2007 to 2016. The primary outcome was a second breast cancer diagnosis within 1 year of mammography. Performance measures included the recall rate, cancer detection rate, interval cancer rate, positive predictive value of biopsy recommendation, sensitivity, and specificity. RESULTS: Among 32 331 women who underwent 117 971 surveillance mammographic examinations (112 269 digital mammographic examinations and 5702 DBT examinations), the mean age at initial diagnosis was 59 years ± 12 (standard deviation). Of 1418 second breast cancers diagnosed, 998 were surveillance-detected cancers and 420 were interval cancers. The recall rate was 8.8% (10 365 of 117 971; 95% CI: 8.6%, 9.0%), the cancer detection rate was 8.5 per 1000 examinations (998 of 117 971; 95% CI: 8.0, 9.0), the interval cancer rate was 3.6 per 1000 examinations (420 of 117 971; 95% CI: 3.2, 3.9), the positive predictive value of biopsy recommendation was 31.0% (998 of 3220; 95% CI: 29.4%, 32.7%), the sensitivity was 70.4% (998 of 1418; 95% CI: 67.9%, 72.7%), and the specificity was 98.1% (114 331 of 116 553; 95% CI: 98.0%, 98.2%). Compared with previously published studies, interval cancer rate was comparable with rates from 1996 to 2007 in women with a personal history of breast cancer and was higher than the published digital mammography screening benchmarks. CONCLUSION: In transitioning from screen-film to digital mammography and digital breast tomosynthesis, surveillance mammography performance demonstrated minimal improvement over time and remained inferior to the performance of screening mammography benchmarks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moy and Gao in this issue

Breast cancer remains the most common nonskin cancer, the second leading cause of cancer deaths, and the leading cause of premature death in US women. Mammography screening has been proven effective in reducing breast cancer deaths in women age 40 years and older. A mortality reduction of 40% is possible with regular screening. Treatment advances cannot overcome the disadvantage of being diagnosed with an advanced-stage tumor. The ACR and Society of Breast Imaging recommend annual mammography screening beginning at age 40, which provides the greatest mortality reduction, diagnosis at earlier stage, better surgical options, and more effective chemotherapy. Annual screening results in more screening-detected tumors, tumors of smaller sizes, and fewer interval cancers than longer screening intervals. Screened women in their 40s are more likely to have early-stage disease, negative lymph nodes, and smaller tumors than unscreened women. Delaying screening until age 45 or 50 will result in an unnecessary loss of life to breast cancer and adversely affects minority women in particular. Screening should continue past age 74 years, without an upper age limit unless severe comorbidities limit life expectancy. Benefits of screening should be considered along with the possibilities of recall for additional imaging and benign biopsy and the less tangible risks of anxiety and overdiagnosis. Although recall and biopsy recommendations are higher with more frequent screening, so are life-years gained and breast cancer deaths averted. Women who wish to maximize benefit will choose annual screening starting at age 40 years and will not stop screening prematurely.

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.

The purpose of this article is to compare commonly used breast cancer risk assessment models, describe the machine learning approach and big data in risk prediction, and summarize the potential benefits and harms of restrictive risk-based screening.The commonly used risk assessment models for breast cancer can be complex and cumbersome to use. Each model incorporates different sets of risk factors, which are weighted differently and can produce different results for the same patient. No model is appropriate for all subgroups of the general population and only one model incorporates mammographic breast density. Future development of risk prediction tools that are generalizable and simpler to use are needed in guiding clinical decisions.

Contains fulltext : 208984.pdf (Publisher’s version ) (Open Access) Multiple studies in the first decade of the 21(st) century have established contrast-enhanced breast MRI as a screening modality for women with a hereditary or familial increased risk for the development of breast cancer. In recent studies, in women with various risk profiles, the sensitivity ranges between 81% and 100%, which is approximately twice as high as the sensitivity of mammography. The specificity increases in follow-up rounds to around 97%, with positive predictive values for biopsy in the same range as for mammography. MRI preferentially detects the more aggressive/invasive types of breast cancer, but has a higher sensitivity than mammography for any type of cancer. This performance implies that in women screened with breast MRI, all other examinations must be regarded as supplemental. Mammography may yield ~5% additional cancers, mostly ductal carcinoma in situ, while slightly decreasing specificity and increasing the costs. Ultrasound has no supplemental value when MRI is used. Evidence is mounting that in other groups of women the performance of MRI is likewise superior to more conventional screening techniques. Particularly in women with a personal history of breast cancer, the gain seems to be high, but also in women with a biopsy history of lobular carcinoma in situ and even women at average risk, similar results are reported. Initial outcome studies show that breast MRI detects cancer earlier, which induces a stage-shift increasing the survival benefit of screening. Cost-effectiveness is still an issue, particularly for women at lower risk. Since costs of the MRI scan itself are a driving factor, efforts to reduce these costs are essential. The use of abbreviated MRI protocols may enable more widespread use of breast MRI for screening. Level of Evidence: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:377-390.

Digital breast tomosynthesis (DBT) has been widely adopted in breast imaging in both screening and diagnostic settings. The benefits of DBT are well established. Compared with two-dimensional digital mammography (DM), DBT preferentially increases detection of invasive cancers without increased detection of in-situ cancers, maximizing identification of biologically significant disease, while mitigating overdiagnosis. The higher sensitivity of DBT for architectural distortion allows increased diagnosis of invasive cancers overall and particularly improves the visibility of invasive lobular cancers. Implementation of DBT has decreased the number of recalls for false-positive findings at screening, contributing to improved specificity at diagnostic evaluation. Integration of DBT in diagnostic examinations has also resulted in an increased percentage of biopsies with positive results, improving diagnostic confidence. Although individual DBT examinations have a longer interpretation time compared with that for DM, DBT has streamlined the diagnostic workflow and minimized the need for short-term follow-up examinations, redistributing much-needed time resources to screening. Yet DBT has limitations. Although improvements in cancer detection and recall rates are seen for patients in a large spectrum of age groups and breast density categories, these benefits are minimal in women with extremely dense breast tissue, and the extent of these benefits may vary by practice environment and by geographic location. Although DBT allows detection of more invasive cancers than does DM, its incremental yield is lower than that of US and MRI. Current understanding of the biologic profile of DBT-detected cancers is limited. Whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation. ©RSNA, 2021.

Contains fulltext : 232884.pdf (Publisher’s version ) (Open Access) PURPOSE: To compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition. METHOD: From September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3T during the same imaging examination. Four breast radiologists (4-13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n=52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUCVGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate. RESULTS: Overall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUCVGC: 0.698, P=0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUCVGC: 0.660, P=0.022 and AUCVGC: 0.700 P=0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUCVGC: 0.427, P=0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P=0.035). Sensitivity and specificity were comparable between both sequences (P=0.760 and P=0.549, respectively). CONCLUSIONS: Despite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.

BACKGROUND: The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. PURPOSE: To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. MATERIALS AND METHODS: This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm(2)). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. RESULTS: Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41–60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59–0.79 vs AUC, 0.75; P = .02–.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm(2)) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm(2); AUC, 0.73–0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17–.87). CONCLUSION: Mean apparent diffusion coefficient calculated with a two–b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.

PURPOSE: To assess the feasibility of using diffusion-time-dependent diffusional kurtosis imaging (tDKI) to measure cellular-interstitial water exchange time (τ(ex)) in tumors, both in animals and in humans. METHODS: Preclinical tDKI studies at 7 T were performed with the GL261 glioma model and the 4T1 mammary tumor model injected into the mouse brain. Clinical studies were performed at 3 T with women who had biopsy-proven invasive ductal carcinoma. tDKI measurement was conducted using a diffusion-weighted STEAM pulse sequence with multiple diffusion times (20–800 ms) at a fixed echo time, while keeping the b-values the same (0–3000 s/mm(2)) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t). RESULTS: Both preclinical and clinical studies showed that, when diffusion time t ≥ 200 ms, D(t) did not have a noticeable change while K(t) decreased monotonically with increasing diffusion time in tumors and t ≥ 100 ms for the cortical ribbon of the mouse brain. The estimated τ(ex) averaged median and interquartile range (IQR) of GL261 and 4T1 tumors were 93 (IQR = 89) ms and 68 (78) ms, respectively. For the cortical ribbon, the estimated τ(ex) averaged median and IQR were 41 (34) ms for C57BL/6 and 30 (17) ms for BALB/c. For invasive ductal carcinoma, the estimated τ(ex) median and IQR of the two breast cancers were 70 (94) and 106 (92) ms. CONCLUSION: The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.

The purpose of this work was to develop a novel method to disentangle the intra- and extracellular components of the total sodium concentration (TSC) in breast cancer from a combination of proton ($$^{1}$$ 1 H) and sodium ($$^{23}\hbox {Na}$$ 23 Na ) magnetic resonance imaging (MRI) measurements. To do so, TSC is expressed as function of the intracellular sodium concentration ($$\hbox {C}_{\text {IC}}$$ C IC ), extracellular volume fraction (ECV) and the water fraction (WF) based on a three-compartment model of the tissue. TSC is measured from $$^{23}\hbox {Na}$$ 23 Na MRI, ECV is calculated from baseline and post-contrast $$^{1}$$ 1 H $$\hbox {T}_{{1}}$$ T 1 maps, while WF is measured with a $$^{1}$$ 1 H chemical shift technique. $$\hbox {C}_{\text {IC}}$$ C IC is then extrapolated from the model. Proof-of-concept was demonstrated in three healthy subjects and two patients with triple negative breast cancer. In both patients, TSC was two to threefold higher in the tumor than in normal tissue. This alteration mainly resulted from increased $$\hbox {C}_{\text {IC}}$$ C IC ($$\sim$$ ∼ 30 mM), which was $$\sim$$ ∼ 130% greater than in healthy conditions (10–15 mM) while the ECV was within the expected range of physiological values (0.2–0.25). Multinuclear MRI shows promise for disentangling $$\hbox {C}_{\text {IC}}$$ C IC and ECV by taking advantage of complementary $$^{1}$$ 1 H and $$^{23}\hbox {Na}$$ 23 Na measurements.

The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3 J. MAGN. RESON. IMAGING 2020;52:1587-1606.

Item does not contain fulltext Screening for breast cancer reduces breast cancer-related mortality and earlier detection facilitates less aggressive treatment. Unfortunately, current screening modalities are imperfect, suffering from limited sensitivity and high false-positive rates. Novel techniques in the field of breast imaging may soon play a role in breast cancer screening: digital breast tomosynthesis, contrast material-enhanced spectral mammography, US (automated three-dimensional breast US, transmission tomography, elastography, optoacoustic imaging), MRI (abbreviated and ultrafast, diffusion-weighted imaging), and molecular breast imaging. Artificial intelligence and radiomics have the potential to further improve screening strategies. Furthermore, nonimaging-based screening tests such as liquid biopsy and breathing tests may transform the screening landscape. (c) RSNA, 2020 Online supplemental material is available for this article.

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which human perception remains invariant. If we can better understand the differences between human and machine perception, we can potentially characterize and mitigate this effect. We therefore propose a framework for comparing human and machine perception in medical diagnosis. The two are compared with respect to their sensitivity to the removal of clinically meaningful information, and to the regions of an image deemed most suspicious. Drawing inspiration from the natural image domain, we frame both comparisons in terms of perturbation robustness. The novelty of our framework is that separate analyses are performed for subgroups with clinically meaningful differences. We argue that this is necessary in order to avert Simpson's paradox and draw correct conclusions. We demonstrate our framework with a case study in breast cancer screening, and reveal significant differences between radiologists and DNNs. We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions. For microcalcifications, DNNs use a separate set of high frequency components than radiologists, some of which lie outside the image regions considered most suspicious by radiologists. These features run the risk of being spurious, but if not, could represent potential new biomarkers. For soft tissue lesions, the divergence between radiologists and DNNs is even starker, with DNNs relying heavily on spurious high frequency components ignored by radiologists. Importantly, this deviation in soft tissue lesions was only observable through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into our comparison framework.

MRI of the breast is the most sensitive test for breast cancer detection and outperforms conventional imaging with mammography, digital breast tomosynthesis, or ultrasound. However, the long scan time and relatively high costs limit its widespread use. Hence, it is currently only routinely implemented in the screening of women at an increased risk of breast cancer. To overcome these limitations, abbreviated dynamic contrast‐enhanced (DCE)‐MRI protocols have been introduced that substantially shorten image acquisition and interpretation time while maintaining a high diagnostic accuracy. Efforts to develop abbreviated MRI protocols reflect the increasing scrutiny of the disproportionate contribution of radiology to the rising overall healthcare expenditures. Healthcare policy makers are now focusing on curbing the use of advanced imaging examinations such as MRI while continuing to promote the quality and appropriateness of imaging. An important cornerstone of value‐based healthcare defines value as the patient's outcome over costs. Therefore, the concept of a fast, abbreviated MRI exam is very appealing, given its high diagnostic accuracy coupled with the possibility of a marked reduction in the cost of an MRI examination. Given recent concerns about gadolinium‐based contrast agents, unenhanced MRI techniques such as diffusion‐weighted imaging (DWI) are also being investigated for breast cancer diagnosis. Although further larger prospective studies, standardized imaging protocol, and reproducibility studies are necessary, initial results with abbreviated MRI protocols suggest that it seems feasible to offer screening breast DCE‐MRI to a broader population. This article aims to give an overview of abbreviated and fast breast MRI protocols, their utility for breast cancer detection, and their emerging role in the new value‐based healthcare paradigm that has replaced the fee‐for‐service model. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:e85–e100.

PURPOSE: To retrospectively assess whether there is an association between screening mammography and the use of a variety of preventive services in women who are enrolled in Medicare. MATERIALS AND METHODS: U.S. Medicare claims from 2010 to 2014 Research Identifiable Files were reviewed to retrospectively identify a group of women who underwent screening mammography and a control group without screening mammography in 2012. The screened group was divided into positive versus negative results at screening, and the positive subgroup was divided into false-positive and true-positive findings. Multivariate logistic regression models and inverse probability of treatment weighting were used to examine the relationship between screening status and the probabilities of undergoing Papanicolaou test, bone mass measurement, or influenza vaccination in the following 2 years. RESULTS: The cohort consisted of 555 705 patients, of whom 185 625 (33.4%) underwent mammography. After adjusting for patient demographics, comorbidities, geographic covariates, and baseline preventive care, women who underwent index screening mammography (with either positive or negative results) were more likely than unscreened women to later undergo Papanicolaou test (odds ratio [OR], 1.49; 95% confidence interval: 1.40, 1.58), bone mass measurement (OR, 1.70; 95% confidence interval: 1.63, 1.78), and influenza vaccine (OR, 1.45; 95% confidence interval: 1.37, 1.53). In women who had not undergone these preventive measures in the 2 years before screening mammography, use of these three services after false-positive findings at screening was no different than after true-negative findings at screening. CONCLUSION: In beneficiaries of U.S. Medicare, use of screening mammography was associated with higher likelihood of adherence to other preventive guidelines, without a negative association between false-positive results and cervical cancer screening. © RSNA, 2018 Online supplemental material is available for this article.

The purpose of this study was to investigate county-level geographic patterns of mammographic screening uptake throughout the United States and to determine the impact of rural versus urban settings on breast cancer screening uptake.This descriptive study used County Health Rankings data to identify the percentage of Medicare enrollees 67-69 years old per county who had at least one mammogram in 2013 or 2012 (uptake). Uptake was matched with U.S. Department of Agriculture Atlas of Rural and Small Town America categorizations along a rural-urban continuum scale from 1 to 9 based on county population size (large urban, population ≥ 20,000 people; small urban,20,000 people) and proximity to a metropolitan area. Univariable and multivariable analyses were performed.In all, 2,243,294 Medicare beneficiaries were eligible for mammograms. National mean uptake per county was 60.5% (range, 26.0-86.0%). Uptake was significantly higher in metropolitan and large urban counties in 25 states and lower in only one. County-level mammographic uptake was moderately positively correlated with percentage of residents with some college education (r = 0.40, p0.001) and moderately negatively correlated with age-adjusted mortality (r = -0.41, p0.001). Multivariable analysis showed that percentage of white and black residents and age-adjusted mortality rate were the strongest significant independent predictors of uptake.Uptake of mammographic screening services in a Medicare population varies widely at the county level and is generally lowest in rural counties and urban counties with fewer than 20,000 people.

OBJECTIVE. The purpose of this study is to describe screening updates for women with average and high risk for breast cancer, compare different screening strategies, and describe new approaches in risk prediction, including radiomics. CONCLUSION. All women are at substantial risk for breast cancer. For women with average risk, annual mammography beginning at 40 years old maximizes the life-extending benefits and provides improved treatment options. Women at higher risk need earlier and more intense screening. Delaying initiation or decreasing frequency of mammographic screening adversely affects breast cancer detection.

Breast density, fibroglandular tissue, and background parenchymal enhancement (BPE) are recognized independent biomarkers for breast cancer risk. For this reason, reproducibility and consistency in objective assessment of these parameters at mammography (breast density) and at magnetic resonance imaging (fibroglandular tissue and BPE) are clinically relevant. However, breast density, fibroglandular tissue, and BPE are manifestations of dynamic physiologic processes and may change in response to both endogenous and exogenous hormonal stimulation. It is therefore important for the radiologist to recognize settings in which hormonal stimulation may alter the appearance of these biomarkers at imaging and to appreciate how such changes may affect risk assessment, cancer detection, and even prognosis. The purpose of this review article is therefore to review key features and means of evaluating breast density, fibroglandular tissue, and BPE at imaging; to detail how endogenous and exogenous hormonal stimuli may affect breast density, fibroglandular tissue, and BPE, potentially affecting radiologic interpretation; and, finally, to provide an update regarding current hormone treatment guidelines and indications that may result in imaging changes through hormone modulation. ©RSNA, 2018.

Rationale and Objectives The present study aims to assess associations of Medicare beneficiary screening mammography rates with local mammography facility and radiologist availability. Materials and Methods Mammography screening rates for Medicare fee-for-service beneficiaries were obtained for US counties using the County Health Rankings data set. County-level certified mammography facility counts were obtained from the United States Food and Drug Administration. County-level mammogram-interpreting radiologist and breast imaging subspecialist counts were determined using Centers for Medicare & Medicaid Services fee-for-service claims files. Spearman correlations and multivariable linear regressions were performed using counties' facility and radiologist counts, as well as counts normalized to counties' Medicare fee-for-service beneficiary volume and land area. Results Across 3035 included counties, average screening mammography rates were 60.5% ± 8.2% (range 26%–88%). Correlations between county-level screening rates and total mammography facilities, facilities per 100,000 square mile county area, total mammography-interpreting radiologists, and mammography-interpreting radiologists per 100,000 county-level Medicare beneficiaries were all weak (r = 0.22–0.26). Correlations between county-level screening rates and mammography rates per 100,000 Medicare beneficiaries, total breast imaging subspecialist radiologists, and breast imaging subspecialist radiologists per 100,000 Medicare beneficiaries were all minimal (r = 0.06–0.16). Multivariable analyses overall demonstrated radiologist supply to have a stronger independent effect than facility supply, although effect sizes remained weak for both. Conclusion Mammography facility and radiologist supply-side factors are only weakly associated with county-level Medicare beneficiary screening mammography rates, and as such, screening mammography may differ from many other health-care services. Although efforts to enhance facility and radiologist supply may be helpful, initiatives to improve screening mammography rates should focus more on demand-side factors, such as patient education and primary care physician education and access.

OBJECTIVES The aim of this study was to assess the applicability of Dixon radial volumetric encoding (Dixon-RAVE) for comprehensive dynamic contrast-enhanced 3D magnetic resonance imaging (MRI) of the breast using a combination of radial sampling, model-based fat/water separation, compressed sensing, and parallel imaging. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act-compliant prospective study, 24 consecutive patients underwent bilateral breast MRI, including both conventional fat-suppressed and non-fat-suppressed precontrast T1-weighted volumetric interpolated breath-hold examination (VIBE). Afterward, 1 continuous Dixon-RAVE scan was performed with the proposed approach while the contrast agent was injected. This scan was immediately followed by the acquisition of 4 conventional fat-saturated VIBE scans. From the comprehensive Dixon-RAVE data set, different image contrasts were reconstructed that are comparable to the separate conventional VIBE scans.Two radiologists independently rated image quality, conspicuity of fibroglandular tissue from fat (FG), and degree of fat suppression (FS) on a 5-point Likert-type scale for the following 3 comparisons: precontrast fat-suppressed (pre-FS), precontrast non-fat-suppressed (pre-NFS), and dynamic fat-suppressed (dyn-FS) images. RESULTS When scores were averaged over readers, Dixon-RAVE achieved significantly higher (P < 0.001) degree of fat suppression compared with VIBE, for both pre-FS (4.25 vs 3.67) and dyn-FS (4.10 vs 3.46) images. Although Dixon-RAVE had lower image quality score compared with VIBE for the pre-FS (3.56 vs 3.67, P = 0.490), the pre-NFS (3.54 vs 3.88, P = 0.009), and the dyn-FS images (3.06 vs 3.67, P < 0.001), acceptable or better diagnostic quality was achieved (score ≥ 3). The FG score for Dixon-RAVE in comparison to VIBE was significantly higher for the pre-FS image (4.23 vs 3.85, P = 0.044), lower for the pre-NFS image (3.98 vs 4.25, P = 0.054), and higher for the dynamic fat-suppressed image (3.90 vs 3.85, P = 0.845). CONCLUSIONS Dixon-RAVE can serve as a one-stop-shop approach for comprehensive T1-weighted breast MRI with diagnostic image quality, high spatiotemporal resolution, reduced overall scan time, and improved fat suppression compared with conventional imaging.

The purpose of this study was to determine the rate, characteristics, and outcomes of discordant MRI-guided vacuum-assisted biopsy (VAB) in women with suspected breast cancer.This retrospective study reviewed 1314 MRI-guided VABs performed in 1211 women between 2007 and 2013 and yielded 25 discordant results in 24 women. MRI characteristics; BI-RADS assessments; whether the lesion was missed, partially sampled, or excised at biopsy; and biopsy and surgical pathology results were reviewed. Statistical analyses were performed using Fisher exact and Mann-Whitney U tests.Among 1314 lesions that underwent MRI-guided VAB, 25 results were discordant (1.9%; 95% CI, 1.2-2.8%), and nine lesions with discordant results (36.0%, 95% CI, 18.5-56.9%) were malignant at surgical excision (three invasive ductal carcinoma and six ductal carcinoma in situ). There was no significant association between malignancy and lesion type, size, enhancement pattern, BI-RADS assessment, or clinical indication. Forty-four percent (11/25) of discordant lesions were missed, 48.0% (12/25) were partially sampled, and 8.0% (2/25) appeared to have been excised. Of the nine malignant lesions, 44.4% (4/9) discordant malignant lesions were missed, 44.4% (4/9) were partially sampled, and 11.1% (1/9) appeared to have been excised. Lesion sizes and types were similar in the missed and partially excised groups.The potential for false-negative results at MRI-guided VAB underscores the importance of radiologic-histologic correlation and imaging review after biopsy. Rebiopsy or excision in discordant cases is therefore recommended.

Women newly diagnosed with stage 1 breast cancer have an early-stage disease that can be effectively treated. Evidence provides little justification for performing imaging to exclude metastasis in asymptomatic women with stage I breast cancer. No differences have been found in survival or quality of life in women regardless of whether they underwent initial workup for metastatic disease. These women generally prefer intensive follow-up to detect an early recurrence. However, survival rates do not differ between women who obtain intensive screening and surveillance, with imaging and laboratory studies, and women who undergo testing only as a result of development of symptoms or findings on clinical examinations. In addition, quality of life is similar for women who undergo intensive surveillance compared with those who do not. American Society of Clinical Oncology and National Comprehensive Cancer Network guidelines state that annual mammography is the only imaging examination that should be performed to detect a localized breast recurrence in asymptomatic patients. Additional imaging may be needed if the patient has locoregional symptoms. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every 3 years by a multidisciplinary expert panel. The guideline development and review by the panel include extensive analysis of current medical literature from peer-reviewed journals and application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures. When evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.

Purpose The purpose was to evaluate the relationship between body mass index (BMI), mammographic breast density, magnetic resonance (MR) background parenchymal enhancement (BPE), and MR fibroglandular tissue (FGT) in women with breast cancer. Methods Our institutional database was queried for patients with preoperative mammography and breast MR imaging. Results There were 573 women eligible for analysis. Elevated BMI was associated with advanced stage of disease ( P =.01), lower mammographic density ( P P P =.005), and nonpalpable lesions ( P =.04). Conclusions Higher BMI was associated with decreased breast density and FGT. Higher BMI was also associated with advanced stage disease and nonpalpable tumors on clinical exam.

PURPOSE To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions. MATERIALS AND METHODS This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions. RESULTS The patients with malignant lesions had significantly (P

Purpose To demonstrate the feasibility of the use of a rapid, noninvasive, in vivo imaging method to measure fatty acid fractions of breast adipose tissue during diagnostic breast magnetic resonance (MR) examinations and to investigate associations between fatty acid fractions in breast adipose tissue and breast cancer status by using this method. Materials and Methods The institutional review board approved this retrospective HIPAA-compliant study and informed consent was waived. Between July 2013 and September 2014, multiple-echo three-dimensional gradient-echo data were acquired for 89 women. Spectra were generated and used to estimate fractions of monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and saturated fatty acid (SFA) in the breast adipose tissue. Analysis of covariance and exact Mann-Whitney tests were used to compare groups and the Spearman rank correlation coefficient was used to characterize the association of each imaging measure with each attribute. Results For postmenopausal women, MUFA was lower (0.38 ± 0.06 vs 0.46 ± 0.10; P.05) and SFA was higher (0.31 ± 0.07 vs 0.19 ± 0.11; P.05) for women with invasive ductal carcinoma than for those with benign tissue. No correlation was found between body mass index (BMI) and fatty acid fractions in breast adipose tissue. In women with benign tissue, postmenopausal women had a higher PUFA (0.35 ± 0.06 vs 0.27 ± 0.05; P.01) and lower SFA (0.19 ± 0.11 vs 0.30 ± 0.12; P.05) than premenopausal women. Conclusion There is a possible link between the presence of invasive ductal carcinoma and fatty acid fractions in breast adipose tissue for postmenopausal women in whom BMI values are not correlated with the fatty acid fractions. (©) RSNA, 2016 Online supplemental material is available for this article.

Background Although previous studies have focused on rural disparities in the use of screening mammography, city-level use throughout the United States has not been well evaluated even though more than 30 million women live in the 500 largest cities. Purpose To evaluate disparities in the city-level use of screening mammography and to identify factors that have an impact on screening utilization. Materials and Methods This retrospective study used data from large publicly available databases, the American Community Survey and Robert Wood Johnson Foundation 500 Cities Project, which includes screening mammography utilization data from the Behavioral Risk Factor Surveillance System. Databases were searched from January to March 2018. The use of screening mammography was evaluated at the city level by census region and division by using the Mann-Whitney

Purpose To assess the association of fatty acid levels in mammary adipose tissue of postmenopausal women with the presence of breast cancer using the Gradient-echo Spectroscopic Imaging (GSI). Materials and methods Unilateral GSI was performed at 3 T in 61 postmenopausal women undergoing breast MRI exams. The study included 19 women with breast cancer, 23 women with benign/high risk lesions, and 19 women with a history of cancer. Voxel-wise spectral analysis of fatty acids was conducted to measure relative portions of monounsaturated (MUFA), polyunsaturated (PUFA), and saturated fatty acids (SFA) in each voxel. The voxels within mammary adipose tissue were automatically selected and their median fatty acid fractions were used for quantitative analysis. Statistical analyses were performed using χ2 test, one-way analysis of variance (ANOVA) with Tukey-Kramer multiple comparison tests, and linear regression. Results Postmenopausal women with malignancies had significantly higher SFA (0.336 ± 0.038) in mammary adipose tissue compared to those with benign disease (0.283 ± 0.046, p = 0.0008) and to those with a history of breast cancer (0.287 ± 0.050, p = 0.0038). Postmenopausal women with malignant lesions had significantly lower MUFA (0.352 ± 0.041) compared to those with benign disease (0.401 ± 0.043, p = 0.0032) and with history of breast cancer (0.388 ± 0.055, p = 0.0484). The history of cancer group had a significant correlation (r = 0.60, p = 0.006) between SFA and BMI, and the cancer group had a significant correlation (r = 0.57, p = 0.010) between PUFA and BMI. Conclusions Fatty acid composition of mammary adipose tissue, particularly higher SFA and lower MUFA, may be associated with breast cancer. The GSI method utilizes an automated voxel-based analysis to measure fatty acid composition, and may be used to assess the role of mammary adipose tissue in cancer development and progress.

The purpose of this study was to assess the rate, type, and severity of complications related to 9-gauge stereotactic vacuum-assisted breast biopsy (SVAB) and to delineate associated factors that may contribute to a higher rate of complications.This retrospective study included 4776 patients who underwent SVAB between 2003 and 2016. A total of 319 patients with documented postbiopsy complications were identified. Complications were subcategorized as bleeding, pain, lightheadedness, bruising, and other complications, and their severity was classified as minor, moderate, or severe. Hematoma volumes were correlated with biopsy location and complication severity. A group of control subjects who underwent SVAB but had no complications was compared with the group of study patients with regard to age, biopsy location, lesion type, and pathologic findings. Postbiopsy screening adherence was assessed. Statistical analyses were performed using the Fisher exact, Mann-Whitney, Kruskal-Wallis, and Spearman rank correlation tests.Of the 319 patients with complications who were identified (representing 6.7% of the 4776 patients who underwent SVAB), 307 (96.2%) had mild complications, 12 (3.8%) had moderate complications, and no patients had severe complications. The most common complication was bleeding or hematoma (89.3% of patients [285/319]), followed by pain (6.9% [22/319]), lightheadedness (0.9% [3/319]), bruising (0.9% [3/319]), and other complications (1.9% [6/319]). No significant differences were noted between the study group and the control group in terms of age (p = 0.474), biopsy location (p = 0.065), histologic findings (p = 0.056), or lesion type (p = 0.568). Hematoma volume (median, 7.5 cmClinically significant complications associated with SVAB were exceedingly rare (0.3%) in this large study spanning 13 years.

The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future.CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence–based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists—especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images. © RSNA, 2019 Online supplemental material is available for this article.

Contains fulltext : 208973.pdf (Publisher’s version ) (Open Access) MRI of the breast has the highest sensitivity for breast cancer detection among current clinical imaging modalities and is indispensable for breast imaging practice. While the basis of breast MRI consists of T1-weighted contrast-enhanced imaging, T2-weighted, ultrafast, and diffusion-weighted imaging may be used to improve lesion characterization. Such multiparametric assessment of breast lesions allows for excellent discrimination between benign and malignant breast lesions. Indications for breast MRI are expanding. In preoperative staging, multiple studies confirm the superiority of MRI to other imaging modalities for tumor size estimation and detection of additional tumor foci in the ipsilateral and contralateral breast. Ongoing studies show that in experienced hands this can be used to improve breast cancer surgery, although there is no evidence of improved long-term outcomes. Screening indications are likewise growing as evidence is accumulating that OncologicRI depicts cancers at an earlier stage than mammography in all women. To manage the associated costs for screening, the use of abbreviated protocols may be beneficial. In patients treated with neoadjuvant chemotherapy, MRI is used to document response. It is essential to realize that oncologic and surgical response are different, and evaluation should be adapted to the underlying question.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a cancer in the breast, when tested on the screening population. We attribute the high accuracy of our model to a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and find our model to be as accurate as experienced radiologists when presented with the same data. Finally, we show that a hybrid model, averaging probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To better understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, model design, training procedure, errors, and properties of its internal representations., Comment: MIDL 2019 [arXiv:1907.08612]