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PURPOSE: The purpose of this study was to evaluate the emotional and financial impact of coronavirus disease 2019 (COVID-19) on breast radiologists to understand potential consequences on physician wellness and gender disparities in radiology. METHODS: A 41-question survey was distributed from June to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Psychological distress and financial loss scores were calculated on the basis of survey responses and compared across gender and age subgroups. A multivariate logistic model was used to identify factors associated with psychological distress scores. RESULTS: A total of 628 surveys were completed (18% response rate); the mean respondent age was 52 ± 10 years, and 79% were women. Anxiety was reported by 68% of respondents, followed by sadness (41%), sleep problems (36%), anger (25%), and depression (23%). A higher psychological distress score correlated with female gender (odds ratio [OR], 1.9; P = .001), younger age (OR, 0.8 per SD; P = .005), and a higher financial loss score (OR, 1.4; P < .0001). Participants whose practices had not initiated wellness efforts specific to COVID-19 (54%) had higher psychological distress scores (OR, 1.4; P = .03). Of those with children at home, 38% reported increased childcare needs, higher in women than men (40% versus 29%, P < .001). Thirty-seven percent reported that childcare needs had adversely affected their jobs, which correlated with higher psychological distress scores (OR, 2.2-3.3; P < .05). CONCLUSIONS: Psychological distress was highest among younger and female respondents and those with greater pandemic-specific childcare needs and financial loss. Practice-initiated COVID-19-specific wellness efforts were associated with decreased psychological distress. Policies are needed to mitigate pandemic-specific burnout and worsening gender disparities.

Objective Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment. Methods A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). “Expert” consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses. Results Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05–0.69), and lowest reproducibility for “minimal.” Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47–0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44–0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20–0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09–0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2–3 vs 4–5) showed moderate agreement with consensus (mean kappa, 0.54; range, −0.06–0.87). Conclusion With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.

Purpose The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. Methods A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. Results The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). Conclusions Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.

The ACR recognizes that radiology practices are grappling with when and how to safely resume routine radiology care during the coronavirus disease 2019 (COVID-19) pandemic. Although it is unclear how long the pandemic will last, it may persist for many months. Throughout this time, it will be important to perform safe, comprehensive, and effective care for patients with and patients without COVID-19, recognizing that asymptomatic transmission is common with this disease. Local idiosyncrasies prevent a single prescriptive strategy. However, general considerations can be applied to most practice environments. A comprehensive strategy will include consideration of local COVID-19 statistics; availability of personal protective equipment; local, state, and federal government mandates; institutional regulatory guidance; local safety measures; health care worker availability; patient and health care worker risk factors; factors specific to the indication(s) for radiology care; and examination or procedure acuity. An accurate risk-benefit analysis of postponing versus performing a given routine radiology examination or procedure often is not possible because of many unknown and complex factors. However, this is the overriding principle: If the risk of illness or death to a health care worker or patient from health care-acquired COVID-19 is greater than the risk of illness or death from delaying radiology care, the care should be delayed; however, if the opposite is true, the radiology care should proceed in a timely fashion.

Objective There is insufficient large-scale evidence for screening mammography in women Methods This retrospective, HIPAA-compliant, institutional review board–exempt study analyzed data from 150 NMD mammography facilities in 31 states. Patients were stratified by 5-year age intervals, availability of prior mammograms, and specific risk factors for breast cancer: family history of breast cancer, personal history of breast cancer, and dense breasts. Four screening performance metrics were calculated for each age and risk group: recall rate (RR), cancer detection rate (CDR), and positive predictive values for biopsy recommended (PPV2) and biopsy performed (PPV3). Results Data from 5,986,131 screening mammograms performed between January 2008 and December 2015 in 2,647,315 women were evaluated. Overall, mean CDR was 3.69 of 1,000 (95% confidence interval: 3.64-3.74), RR was 9.89% (9.87%-9.92%), PPV2 was 20.1% (19.9%-20.4%), and PPV3 was 28.2% (27.0%-28.5%). Women aged 30 to 34 and 35 to 39 had similar CDR, RR, and PPVs, with the presence of the three evaluated risk factors associated with significantly higher CDR. Moreover, compared with a population currently recommended for screening mammography in the United States (aged 40-49 at average risk), incidence screening (at least one prior screening examination) of women aged 30 to 39 with the three evaluated risk factors has similar cancer detection rates and recall rates. Discussion Women with one or more of these three specific risk factors likely benefit from screening commencing at age 30 instead of age 40.

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and 99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.

PURPOSE: To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting. METHODS: We conducted a retrospective Institutional Review Board-approved study on a case-control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination (including mediolateral oblique [MLO] view and craniocaudal [CC] view two images) was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case-control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer-free (controls) within the follow-up period. We implemented an end-to-end deep learning model and a GoogLeNet-LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different sub-regions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric. RESULTS: The highest AUC was 0.73 (95% Confidence Interval [CI]: 0.68 – 0.78; GoogLeNet-LDA model on CC view) when using the whole-breast and was 0.72 (95% CI: 0.67– 0.76; GoogLeNet-LDA model on MLO+CC view) when using the dense tissue, respectively, as the model input. The GoogleNet-LDA model significantly (all p

ORIGINAL RESEARCH ABSTRACT PURPOSE The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER− tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema ( P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.

Deep learning models are traditionally trained purely in a data-driven approach; the information for the model training usually only comes from a single source of the training data. In this work, we investigate how to supply additional clinical knowledge that is associated with the training data. Our goal is to train deep learning models for breast cancer diagnosis using mammogram images. Along with the main classification task between clinically proven cancer vs negative/benign cases, we design two auxiliary tasks each capturing a form of additional knowledge to facilitate the main task. Specifically, one auxiliary task is to classify images according to the radiologist-made BI-RADS diagnosis scores and the other auxiliary task is to classify images in terms of the BI-RADS breast density categories. We customize a Multi-Task Learning model to jointly perform the three tasks (main task and two auxiliary tasks). We test four deep learning architectures: CBR–Tiny, ResNet18, GoogleNet, and DenseNet and we investigate the benefit of incorporating such knowledge over ImageNet pre-trained models and in the case of randomly initialized models. We run experiments on an internal dataset consisting of screening full field digital mammography images for a total of 1,380 images (341 cancer and 1,039 negative or benign). Our results show that, by adding clinical knowledge conveyed through the two auxiliary tasks to the training process, we can improve the performance of the target task of breast cancer diagnosis, thus highlighting the benefit of incorporating clinical knowledge into data-driven learning to enhance deep learning model training.

Our ultimate goal is to develop a personalized breastscreening model. This model will provide information to an individual woman for planning her own screening options based on her imaging and other characteristics. To do so, we are developing a computerized method to estimate a near term risk of screen detectable breast cancer (sBCa) by analyzing lateral and temporal differences in screening mammograms using the Radon Cumulative Distribution Transform (RCDT), a deep convolutional neural network (dCNN). The RCDT can highlight subtle signals associated with future sBCa by exploiting lateral and temporal differences in screening mammograms of women. We used a dCNN to build a robust classification marker of having sBCa within a year from a negative screening. We explored the potential for incremental improvement over known risk factors by combining the dCNN with age and showed consistent gains in identifying asymptomatic women with a low risk of having sBCa (who could use the personalized screening option to safely skip their next screening). We used 4296 screening mammogram images of 537 women with 151 sBCa cases and fine-tuned the VGG16 network using a data split ratio of 0.64:0.16:0.2 for training, validation, and testing. The final model on the independent testing dataset achieved an AUC of 0.82, and a specificity at a sensitivity level of 98% (Sp@Se98) of 0.51 with a 95% confidence interval of 0.40 to 0.65. This indicates that the model could reliably identify at least 40% of low-risk women with a 2% error for a false negative rate.

Breast cancer risk prediction is becoming increasingly important especially after recent advances in deep learning models. In breast cancer screening, it is common that patients have multiple longitudinal mammogram examinations, where the longitudinal imaging data may provide additional information to boost the learning of a risk prediction model. In this study, we aim to leverage quantitative imaging features extracted from prior mammograms to augment the training of a risk prediction model, through two technical approaches: 1) prior data-enabled transfer learning, and 2) multi-task learning. We evaluated the two approaches on a study cohort of 306 patients in a case-control setting, where each patient has 3 longitudinal screening mammogram examinations. Our results show that both two approaches improved the 1-, 2-, and 3-year risk prediction, indicating that additional knowledge can be learned by our approaches from longitudinal imaging data to improve near-term risk prediction.

Objective To assess the impact of the COVID-19 pandemic on breast imaging facilities’ operations and recovery efforts across North America. Methods A survey on breast imaging facilities’ operations and strategies for recovery during the COVID-19 pandemic was distributed to the membership of the Society of Breast Imaging and National Consortium of Breast Centers from June 4, 2020, to July 14, 2020. A descriptive summary of responses was performed. Comparisons were made between demographic variables of respondents and questions of interest using a Pearson chi-square test. Results There were 473 survey respondents (response rate of 13%). The majority of respondents (70%; 332/473) reported 80%–100% breast imaging volume reduction, with 94% (447/473) reporting postponement of screening mammography. The majority of respondents (97%; 457/473) continued to perform biopsies. There were regional differences in safety measures taken for staff (P = 0.004), with practices in the West more likely reporting no changes in the work environment compared to other regions. The most common changes to patients’ experience included spacing out of furniture in waiting rooms (94%; 445/473), limiting visitors (91%; 430/473), and spacing out appointments (83%). A significantly higher proportion of practices in the Northeast (95%; 104/109) initiated patient scheduling changes compared to other regions (P = 0.004). Conclusion COVID-19 had an acute impact on breast imaging facilities. Although common national operational patterns emerged, geographic variability was notable in particular in recovery efforts. These findings may inform future best practices for delivering breast imaging care amid the ongoing and geographically shifting COVID-19 pandemic.

Background The literature supports the use of short-interval follow-up as an alternative to biopsy for lesions assessed as probably benign, Breast Imaging Reporting and Data System (BI-RADS) category 3, with an expected malignancy rate of less than 2%. Purpose To assess outcomes from 6-, 12-, and 24-month follow-up of probably benign findings first identified at recall from screening mammography in the National Mammography Database (NMD). Materials and Methods This retrospective study included women recalled from screening mammography with BI-RADS category 3 assessment at additional evaluation from January 2009 through March 2018 from 471 NMD facilities. Only the first BI-RADS category 3 occurrence for women aged 25 years or older with no personal history of breast cancer was analyzed, with biopsy or 2-year imaging follow-up. Cancer yield and positive predictive value of biopsies performed (PPV3) were determined at each follow-up. Results Among 45 202 women (median age, 55 years; range, 25-90 years) with a BI-RADS category 3 lesion, 1574 (3.5%) underwent biopsy at the time of lesion detection, yielding 72 cancers (cancer yield, 4.6%; 72 of 1574 women). For the remaining 43 628 women who accepted surveillance, 922 were seen within 90 days (with 78 lesions biopsied and 12 [15%] classified as malignant). The women still in surveillance (31 465 of 43 381 women [72.5%]) underwent follow-up mammography at 6 months. Of 3001 (9.5%) lesions biopsied, 456 (15.2%) were malignant (cancer yield, 1.5%; 456 of 31 465 women; 95% confidence interval [CI]: 1.3%, 1.6%). Among 18 748 of 25 997 women (72.1%) in surveillance who underwent follow-up at 12 months, 1219 (6.5%) underwent biopsy with 230 (18.9%) malignant lesions found (cancer yield, 1.2%; 230 of 18 748 women; 95% CI: 1.1%, 1.4%). Through 2-year follow-up, the biopsy rate was 11.2% (4894 of 43 628 women) with a cancer yield of 1.86% (810 malignancies found among 43 628 women; 95% CI: 1.73%, 1.98%) and a PPV3 of 16.6% (810 malignancies found among 4894 women). Conclusion In the National Mammography Database, Breast Imaging Reporting and Data System (BI-RADS) category 3 use is appropriate, with 1.86% cumulative cancer yield through 2-year follow-up. Of 810 malignancies, 468 (57.8%) were diagnosed at or before 6 months, validating necessity of short-interval follow-up of mammographic BI-RADS category 3 findings. © RSNA, 2020

Purpose: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy. Experimental Design: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy. Results: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset. Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.

Purpose To determine rate of malignancy at stereotactic biopsy of amorphous calcifications with different distributions using current imaging, clinical, and histopathologic criteria. Materials and Methods From January 2009 to September 2013, this retrospective study reviewed a large set of stereotactic biopsies to identify amorphous calcifications and their clinical, imaging, and histopathologic characteristics. Calcification distribution was correlated with malignancy rate after adjusting for known risk factors using logistic regression. Results Of 1903 sequential biopsies, 546 (28.7%) were for amorphous calcifications. After excluding atypical lesions not excised and patients with more than one biopsy in the same year, 497 lesions from 494 women (median age, 52 years; age range, 30-81 years) remained. Fifty-two (10.5%; 95% confidence interval [CI]: 7.9, 13.5) lesions proved malignant, with 17 of 52 (42.7%) being invasive cancers (median, 0.3 cm; range, 0.1-1.3 cm) and all 17 of them being estrogen and progesterone receptor positive and node negative. Malignancy rates in a segmental (six of 21 [28.6%]), linear (eight of 32 [25.0%]), or multiple group same quadrant (nine of 36 [25.0%]) distribution were significantly higher than malignancy rate in a solitary group of amorphous calcifications (25 of 356 [7.0%]) (P = .004, P = .003, and P = .002, respectively). Of 356 grouped amorphous calcifications, 102 (28.7%) yielded atypical results prompting excision, with three of 102 (2.9%) upgraded to ductal carcinoma in situ at excision. In women younger than 50 years without a personal history of cancer, grouped amorphous calcifications showed four of 127 (3.1%) (95% CI: 0.9, 7.9) were malignant and 39 of 127 (30.7%) were atypical at final histopathology. Conclusion Biopsy of amorphous calcifications remains necessary, with an overall malignancy rate of 10.5%; only 17 of 497 (3.4%) biopsies showed invasive cancer, and all of these were estrogen and progesterone receptor positive. Grouped amorphous calcifications in women younger than 50 years without history of breast or ovarian cancer showed a low malignancy rate of 3.1% (four of 127).

To preliminarily asses if Contrast Enhanced Digital Mammography (CEDM) can accurately reduce biopsy rates for soft tissue BI-RADS 4A or 4B lesions.Eight radiologists retrospectively and independently reviewed 60 lesions in 54 consenting patients who underwent CEDM under Health Insurance Portability and Accountability Act compliant institutional review board-approved protocols. Readers provided Breast Imaging ReportingData System ratings sequentially for digital mammography/digital breast tomosynthesis (DM/DBT), then with ultrasound, then with CEDM for each lesion. Area under the curve (AUC), true positive rates and false positive rates, positive predictive values and negative predictive values were calculated. Statistical analysis accounting for correlation between lesion-examinations and between-reader variability was performed using OR/DBM (for SAS v.3.0), generalized linear mixed model for binary data (proc glimmix, SAS v.9.4, SAS Institute, Cary North Carolina), and bootstrap.The cohort included 49 benign, two high-risk and nine cancerous lesions in 54 women aged 34-74 (average 50) years. Reader-averaged AUC for CEDM was significantly higher than DM/DBT alone (0.85 versus 0.66, p0.001) or with US (0.85 versus 0.75, p = 0.001). CEDM increased true positive rates from 0.74 under DB/DBT, and 0.89 with US, to 0.90 with CEDM, (p = 0.019 DM/DBT versus CEDM, p = 0.78 DM/DBT + US versus CEDM) and decreased false positive rates from 0.47 using DM/DBT and 0.61 with US to 0.39 with CEDM (p = 0.017 DM/DBT versus CEDM, p = 0.001 DM/DBT+ US versus CEDM). For an expected cancer rate of 10%, CEDM positive predictive values was 20.5% (95% CI: 16%-27%) and negative predictive values 98.3% (95% CI: 96%-100%).Addition of CEDM for evaluation of low-moderate suspicion soft tissue breast lesions can substantially reduce biopsy of benign lesions without compromising cancer detection.

Breast cancer risk prediction refers to the task of predicting whether a healthy patient is likely to develop breast cancer in the future. Breast density and parenchymal texture features are well-known imaging-based breast cancer risk markers that can be qualitatively/visually assessed by radiologists or even quantitatively measured by computerized software. Recently, deep learning has emerged as a promising strategy to solve tasks in a variety of classification and prediction scenarios, including breast imaging. Building on this premise, we propose a deep learning-based modeling method for breast cancer risk prediction in a case-control setting purely using prior normal screening mammogram images. In addition, considering the fact that clinical statistics shows that the upper outer quadrant is the most common site of origin for breast cancer, we designed a simple experiment on 226 patients (a total of 1,632 images) to explore the concept of localized breast cancer risk prediction. We built two deep learning models with the same settings but fed one with the top halves of the mammogram images (corresponding to the outer portion of a breast) and the other with the bottom halves (corresponding to the inner portion of a breast). Our preliminary results showed that the top halves have a higher prediction performance (AUC=0.89) than the bottom halves (AUC=0.69) in predicting the case/control outcome. This indicates a relation between localized imaging features extracted from a sub-region of the full mammogram images and the underlying risk of developing breast cancer in this specific sub-region.

Digital breast tomosynthesis (DBT) is beginning to be used more frequently alongside full-field digital mammography (DM) in routine breast cancer screening. However, little is known about radiologists’ search strategies reading DBT. This study aims to measure radiologists’ eye movements prior to testing search strategies intended to make DBT faster and/or more accurate. Twelve observers (board certified breast radiologists or current women’s imaging fellows) were instructed to search for lesions as they would report during normal clinical conditions. Observers were shown a single view of a single breast for each case and informed that this was an enriched study (10 positive cases out 20). Eye tracking used a SMI RED250mobile Eye Tracker sampling at 250Hz. Tracking error was below 0.5 deg. There was an increase in the detection rate/accuracy and decrease in false positives with DBT compared to DM. There was a longer search time in DBT compared to DM, with a shorter saccade length shown in DM. Different observers where shown to have differing search patterns however it was not possible to show any observer to have a true driller or scanner technique. Results generally replicated previous studies however; more work would be needed to obtain conclusions as to optimal search strategies.

BACKGROUND. Persistent breast pain (PBP) is prevalent among breast cancer survivors and has powerful negative psychological consequences. The present study provided a first test of the hypothesis that: a) pain catastrophizing, b) heightened perceived risk of cancer, and c) worry that pain indicates cancer may be independent mediating links between breast cancer survivors’ experiences of PBP and heightened emotional distress. METHODOLOGY. We assessed levels of PBP and psychological factors in breast cancer survivors (Survivor Group: n=417; Stages I-IIIA; White=88.7%; Age M=59.4 years) at their first surveillance mammogram post-surgery (6–15 months). A comparison group of women without histories of breast surgery or cancer (Non-cancer Group: n=587; White=78.7%; Age M=57.4 years) was similarly assessed at the time of a routine screening mammogram. All women completed measures of: breast pain, pain catastrophizing, perceived breast cancer risk, and worry that breast pain indicates cancer, as well as measures of emotional distress (symptoms of anxiety, symptoms of depression, and mammography-specific distress). Analyses included race, age, BMI, education, and menopausal status as covariates, with significance set at 0.05. RESULTS. As expected, PBP prevalence was significantly higher in the Survivor Group than in the Non-cancer Group (50.6% vs. 17.5%). PBP+ survivors also had significantly higher levels of emotional distress, pain catastrophizing, mammography-specific distress, and worry that breast pain indicates cancer, compared to PBP- survivors. Structural equation modeling results were significant for all hypothesized mediational pathways. Interestingly, comparisons of PBP+ to PBP- women in the Non-cancer Group showed similar results. CONCLUSION. These findings suggest the importance of: a) pain catastrophizing, b) perceived breast cancer risk, c) and worry that breast pain may indicate cancer, as potential targets for interventions aimed at reducing the negative psychological impact of persistent breast pain in post-surgery breast cancer survivors, as well as in unaffected women with PBP due to unknown reasons.

Objectives We present a retrospective review of 111 breast core biopsy specimens with flat epithelial atypia (FEA) and corresponding excision to better understand rates of upgrade following this diagnosis. Methods In total, 252 breast core biopsy specimens were identified. Cases were excluded if biopsy slides or excision results were unavailable, for ipsilateral carcinoma or other findings warranting excision, or biopsy performed for indications other than mammographically detected calcifications. Coincident atypical lobular hyperplasia was not excluded. Diagnoses were confirmed by breast pathologists, and mammographic images were reviewed. Results Ultimately, 111 biopsy specimens with FEA were included. In subsequent excisions, one (1%) of 111 showed invasive carcinoma, 20 (18%) atypical ductal hyperplasia, 20 (18%) lobular neoplasia, 31 (28%) FEA, and 39 (35%) no atypical findings. Conclusions FEA on core biopsy specimens is associated with a low rate of upgrade to invasive carcinoma. Close follow-up may be a reasonable alternative to excision for isolated FEA on core needle biopsy specimens.

This study compared persistent breast pain among women who received breast-conserving surgery for breast cancer and women without a history of breast cancer.Breast cancer survivors (n=200) were recruited at their first postsurgical surveillance mammogram (6 to 15 mo postsurgery). Women without a breast cancer history (n=150) were recruited at the time of a routine screening mammogram. All women completed measures of breast pain, pain interference with daily activities and intimacy, worry about breast pain, anxiety symptoms, and depression symptoms. Demographic and medical information were also collected.Persistent breast pain (duration ≥6 mo) was reported by 46.5% of breast cancer survivors and 12.7% of women without a breast cancer history (P0.05). Breast cancer survivors also had significantly higher rates of clinically significant persistent breast pain (pain intensity score ≥3/10), as well as higher average breast pain intensity and unpleasantness scores. Breast cancer survivors with persistent breast pain had significantly higher levels of depressive symptoms, as well as pain worry and interference, compared with survivors without persistent breast pain or women without a breast cancer history. Anxiety symptoms were significantly higher in breast cancer survivors with persistent breast pain compared with women without a breast cancer history.Results indicate that persistent breast pain negatively impacts women with a history of breast-conserving cancer surgery compared with women without that history. Strategies to ameliorate persistent breast pain and to improve adjustment among women with persistent breast pain should be explored for incorporation into standard care for breast cancer survivors.

Purpose The National Mammography Database (NMD) contains nearly 20 million examinations from 693 facilities; it is the largest information source for use and effectiveness of breast imaging in the United States. NMD collects demographic, imaging, interpretation, biopsy, and basic pathology results, enabling facility and physician comparison for quality improvement. However, NMD lacks treatment and clinical outcomes data. The network of state cancer registries (CRs) contains detailed pathologic, treatment, and clinical outcomes data. This pilot study assessed electronic linkage of NMD and CR data at a multicenter institution as proof of concept. Materials and Methods We obtained Quality Oversight Committee approval for this retrospective study. Data of patients diagnosed with breast cancer in 2014 and 2015 were retrieved from our NMD-approved radiology information system (RIS) and matched with reportable patients in our CR using social security number (SSN), first name (fname), last name (lname), and date of birth (DOB). Matching was repeated without SSN. Percentage and reasons for mismatch were evaluated. Results The RIS query identified 1,316 patients. CR linkage was 99.2% successful (n = 1,305 of 1,316) using SSN, fname, lname, and DOB. Eleven mismatches included four CR case-finding failures, one NMD fname error, five nonreportable in the CR, and one with correct identifiers in both databases. Without SSN, linkage was 97.3% successful (n = 1,281 of 1,316); name errors accounted for 19 and DOB accounted for 5 additional mismatches. Conclusion Using common data elements, linkage between the NMD and state CRs may be feasible and could provide critical outcomes information to advance accurate assessment of breast imaging in the United States.

Interpretations of breast ultrasound screening examinations result in high recall rates and large inter-radiologist variability, frequently leading to “conservative” recommendations. Double reading of all breast ultrasound screening examinations is cost prohibitive, but double reading of only “initially recalled” cases may prove efficacious. We assessed changes in recommendations, if any, by providing a consensus second opinion in a limited subset of examinations initially recommended for recall. We performed a retrospective reader study with 197 ultrasound examinations (97 not recalled and 100 recalled clinically). First, we generated a consensus “second opinion” consisting of the majority vote of three independent readings of each case by experienced ultrasound interpreters. During the reader study that followed, if the reader recommended a “recall” and the “consensus second opinion” did not, a message to that effect was displayed and the reader was asked to re-review the exam and re-assess if, knowing the second opinion, a re-rating of the case was warranted. We compared performance levels pre- and post- the second opinion. The second opinion resulted in “no recall” recommendations of 141 cases in the entire set, including four cancer cases missed by all three readers. On average, radiologists received “warning” messages in 30 cases (range 15-50), or in ~15% of cases. Rating changes (downgrades to no recall) occurred in 36 of these cases. These changes resulted in a possible recall rate reduction of 28% in prompted cases or 14% overall recall reduction, while increasing the false negative rate by only one case missed by 2 readers (~1%).

Rationale and Objectives Assess results of a prospective, single-site clinical study evaluating digital breast tomosynthesis (DBT) during baseline screening mammography. Materials and Methods Under an institutional review board–approved Health Insurance Portability and Accountability Act (HIPAA)-compliant protocol, consenting women between ages 34 and 56 years scheduled for their initial and/or baseline screening mammogram underwent both full field digital mammography (FFDM) and DBT. The FFDM and the FFDM plus DBT images were interpreted independently in a reader by mode balanced approach by two of 14 participating radiologists. A woman was recalled for a diagnostic work-up if either radiologist recommended a recall. We report overall recall rates and related diagnostic outcome from the 1080 participants. Proportion of recommended recalls (Breast Imaging Reporting and Data System 0) were compared using a generalized linear mixed model (SAS 9.3) with a significance level of P = .0294. Results The fraction of women without breast cancer recommended for recall using FFDM alone and FFDM plus DBT were 412 of 1074 (38.4%) and 274 of 1074 (25.5%), respectively ( P P Conclusions The use of FFDM plus DBT resulted in a significant decrease in recall rates during baseline screening mammography with no reduction in sensitivity.

The purpose of this study was to compare outcomes of radioactive seed localization (RSL) versus wire localization using surgical margin size, reexcision and reoperation rates, specimen size, radiology resource utilization, and cosmesis as measures.Patients who underwent RSL before segmental mastectomy from April 1, 2011, to March 1, 2012, for biopsy-proven cancer were selected. Each was matched using tumor size, type, and surgeon to a wire localization control case, resulting in 232 cases. Width of the closest surgical margin, reexcision rate, and reoperation rate were compared as were the ratios of tumor volume to initial surgical specimen volume and tumor volume to all surgically excised volume (including reexcisions and reoperations). Cosmetic outcome was analyzed by comparison of Harvard scores and specimen volume with breast volume. Radiology resource utilization was compared before and after RSL implementation.No significant differences between methods were found in closest surgical margin (RSL mean, 0.45 cm; wire localization mean, 0.45 cm; p=0.972), reexcision rate (RSL mean, 21.1%; wire localization mean, 26.3%; p=0.360), reoperation rate (RSL, 11.4%; wire localization, 12.7%; p=0.841), ratio of the tumor volume to initial surgical specimen volume (RSL mean, 0.027; wire localization mean, 0.028; p=0.886), ratio of the tumor volume to total volume resected (RSL mean, 0.024; wire localization mean, 0.024; p=0.997), or in clinical or computed cosmesis scores (clinical p=0.5; calculated p=0.060). There was a 34% increase in scheduled biopsy slot utilization, 50% savings in time spent scheduling, and a 4.1-day average decrease in biopsy wait time after RSL institution.RSL is an acceptable alternative to wire localization and offers significant improvements in workflow.

Background In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Methods Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff’s α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman’s rank correlation coefficients were used to compare HEIP and CEIP. Results Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p

Background Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor. Methods Multiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions. Results Each of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways. Conclusions We interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors.

The purpose of this study was to determine the frequency of carcinoma identified with secondary presurgical imaging-guided percutaneous biopsy for patients with high-risk pathologic findings at primary percutaneous biopsy.A retrospective computerized search of our breast imaging database from January 1, 2005, to October 1, 2010, was conducted to identify the cases of patients with high-risk pathologic findings at primary biopsy who underwent at least one secondary biopsy within 9 months and underwent surgical excision. Lesion type, location, biopsy guidance, device, number of samples, and histopathologic findings were recorded. The pathologic findings at surgical excision were compared with the percutaneous biopsy results.One hundred twenty-three patients underwent 158 secondary biopsies. On average, 1.3 additional biopsies were performed per patient. Among the 158 secondary biopsies, 105 (66.4%) had benign, 48 (30.4%) had high-risk, and five (3.2%) had malignant histopathologic findings. No malignant tumors were identified at secondary percutaneous biopsies in cases in which the same lesion type was biopsied in the same quadrant. Use of secondary percutaneous biopsy led to detection of nine malignant tumors in 7.3% of patients-with percutaneous biopsy in four (3.2%) patients and at surgical excision in five (4.1%) patients.After initial imaging-guided percutaneous biopsy reveals high-risk pathologic findings, identification of additional high-risk lesions at secondary percutaneous biopsy is common, and detection of malignancy is relatively infrequent. However, because the secondarily identified high-risk lesions are sometimes upstaged to cancer at surgical excision, additional imaging-directed biopsy has potential benefit for patients with known highrisk lesions and multiple synchronous findings.

We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.

We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions. The reconstructions produced a range of image quality from smooth/low-noise to sharp/high-noise, which had a range in classifier performance corresponding to AUCs of 0.62 to 0.96. Six experienced Mammography Quality Standards Act (MQSA) radiologists rated the likelihood of malignancy for each lesion. We conducted an additional reader study with the same radiologists and a subset of 30 lesions. Radiologists ranked each reconstruction according to their preference. There was disagreement among the six radiologists on which reconstruction produced images with the highest diagnostic content, but they preferred the midsharp/noise image appearance over the others. However, the reconstruction they preferred most did not match with their performance. Due to these disagreements, it may be difficult to develop a single image-based model observer that is representative of a population of radiologists for this particular imaging task.

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

To improve efficacy of breast cancer screening and prevention programs, it requires a risk assessment model with high discriminatory power. This study aimed to assess classification performance of using computed bilateral mammographic density asymmetry to predict risk of individual women developing breast cancer in near-term. The database includes 451 cases with multiple screening mammography examinations. The first (baseline) examinations of all case were interpreted negative. In the next sequential examinations, 187 cases developed cancer or surgically excised high-risk lesions, 155 remained negative (not-recalled), and 109 were recalled benign cases. From each of two bilateral cranio-caudal view images acquired from the baseline examination, we computed two features of average pixel value and local pixel value fluctuation. We then computed mean and difference of each feature computed from two images. When applying the computed features and other two risk factors (woman’s age and subjectively rated mammographic density) to predict risk of cancer development, areas under receiver operating characteristic curves (AUC) were computed to evaluate the discriminatory/classification performance. The AUCs are 0.633±0.030, 0.535±0.036, 0.567±0.031, and 0.719±0.027 when using woman’s age, subjectively rated, computed mean and asymmetry of mammographic density, to classify between two groups of cancer-verified and negative cases, respectively. When using an equal-weighted fusion method to combine woman’s age and computed density asymmetry, AUC increased to 0.761±0.025 (p

To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database.The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database.The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups.This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.

One issue in the adoption of autologous fat transfer to the breast is concern over mammographic changes that may obscure cancer detection. The authors compared mammographic changes following fat grafting to the breast with changes seen after breast reduction.: Twenty-seven women who had normal preoperative mammograms were treated with fat grafting to the breast, including admixing of autologous adipose stem cells with the fat graft, for cosmetic augmentation. Repeated mammograms were performed 12 months after surgery. As a control group, postsurgical mammograms from 23 reduction mammaplasty patients were compared. Eight academic breast imaging radiologists reviewed each mammogram in a blinded fashion. Outcomes analysis accounting for individual radiologist's tendencies was performed using generalized estimating equations.: The average volume of fat injected per patient was 526.5 cc. Fifty mammograms (27 lipotransfer, 23 breast reduction) were assessed. Differences in interpretation among individual radiologists were consistently observed (p0.10). Differences in abnormality rates were nonsignificant for oil cysts, benign calcifications, and calcifications warranting biopsy. Scarring (p0.001) and masses requiring biopsy (p0.001) were more common in the reduction cohort. Breast Imaging Reporting and Data System scores were higher after breast reduction (p0.001). Significant differences in the recommended follow-up time were also seen (p0.01).: Compared with reduction mammaplasty, a widely accepted procedure, fat grafting to the breast produces fewer radiographic abnormalities with a more favorable Breast Imaging Reporting and Data System score and less aggressive follow-up recommendations by breast radiologists.: Therapeutic, III.

Rationale and Objectives The aim of this study was to retrospectively compare the interpretive performance of synthetically reconstructed two-dimensional images in combination with digital breast tomosynthesis (DBT) versus full-field digital mammography (FFDM) plus DBT. Materials and Methods Ten radiologists trained in reading tomosynthesis examinations interpreted retrospectively, under two modes, 114 mammograms. One mode included the directly acquired full-field digital mammograms combined with DBT, and the other included synthetically reconstructed projection images combined with DBT. The reconstructed images do not require additional radiation exposure. The two modes were compared with respect to sensitivity, namely, recommendation to recall a breast with either a pathology-proven cancer ( n = 48) or a high-risk lesion ( n = 6), and specificity, namely, no recommendation to recall a breast not depicting an abnormality ( n = 144) or depicting only benign abnormalities ( n = 30). Results The average sensitivity for FFDM with DBT was 0.826, compared to 0.772 for synthetic FFDM with DBT (difference, 0.054; P = .017 and P = .053 for fixed and random reader effects, respectively). The proportions of breasts with no or benign abnormalities recommended to be recalled were virtually the same: 0.298 and 0.297 for the two modalities, respectively (95% confidence intervals for the difference, −0.028 to 0.036 and −0.070 to 0.066 for fixed and random reader effects, respectively). Sixteen additional clusters of microcalcifications (“positive” breasts) were missed by all readers combined when interpreting the mode with synthesized images versus FFDM. Conclusions Lower sensitivity with comparable specificity was observed with the tested version of synthetically generated images compared to FFDM, both combined with DBT. Improved synthesized images with experimentally verified acceptable diagnostic quality will be needed to eliminate double exposure during DBT-based screening.

Rationale and Objectives The aim of this study was to preliminarily assess the performance of a new, resonance-frequency electrical impedance spectroscopy (REIS) system in identifying young women who were recommended to undergo breast biopsy following imaging. Materials and Methods A seven-probe REIS system was designed and assembled and is currently being prospectively tested. During examination, contact is made with the nipple and six concentric points on the breast skin. Signal sweeps are performed, and outputs ranging from 200 to 800 kHz at 5-kHz intervals are recorded. An initial set of 140 patients, including 56 who eventually had biopsies, 63 who had negative results on screening mammography, and 21 recalled for additional imaging but later determined to have negative results, was used. An initial set of 35 features, 33 representing impedance signal differences between breasts and two representing participant age and average breast density, was assembled and reduced by a genetic algorithm to 14. The performance of an artificial neural network–based classifier was assessed using a case-based leave-one-out method. Results The substantially greater asymmetry between signals of mirror-matched regions ascertained from biopsy (“positive”) compared to nonbiopsy (“negative”) cases resulted in an artificial neural network classifier performance (area under the curve) of 0.830 ± 0.023. At 90% specificity, this classifier, optimized for “recommendation for biopsy” rather than “cancer,” detected 30 REIS-positive cases (54%), including six of nine (67%) actual cancer cases and six of nine women (67%) recommended for surgical excision of high-risk lesions. Conclusions Asymmetry in impedance measurements between bilateral breasts may provide valuable discriminatory information regarding the presence of highly suspicious imaging-based findings.

The purpose of this article is to compare the ability of digital breast tomosynthesis and full field digital mammography (FFDM) to detect and characterize calcifications.One hundred paired examinations were performed utilizing FFDM and digital breast tomosynthesis. Twenty biopsy-proven cancers, 40 biopsy-proven benign calcifications, and 40 randomly selected negative screening studies were retrospectively reviewed by five radiologists in a crossed multireader multimodal observer performance study. Data collected included the presence of calcifications and forced BI-RADS scores. Receiver operator curve analysis using BI-RADS was performed.Overall calcification detection sensitivity was higher for FFDM (84% [95% CI, 79-88%]) than for digital breast tomosynthesis (75% [95% CI, 70-80%]). [corrected] In the cancer cohort, 75 (76%) of 99 interpretations identified calcification in both modes. Of those, a BI-RADS score less than or equal to 2 was rendered in three (4%) and nine (12%) cases with FFDM and digital breast tomosynthesis, respectively. In the benign cohort, 123 (62%) of 200 interpretations identified calcifications in both modes. Of those, a BI-RADS score greater than or equal to 3 was assigned in 105 (85%) and 93 (76%) cases with FFDM and digital breast tomosynthesis, respectively. There was no significant difference in the nonparametric computed area under the receiver operating characteristic curves (AUC) using the BI-RADS scores (FFDM, AUC = 0.76 and SD = 0.03; digital breast tomosynthesis, AUC = 0.72 and SD = 0.04 [p = 0.1277]).In this small data set, FFDM appears to be slightly more sensitive than digital breast tomosynthesis for the detection of calcification. However, diagnostic performance as measured by area under the curve using BI-RADS was not significantly different. With improvements in processing algorithms and display, digital breast tomosynthesis could potentially be improved for this purpose.

Current day digital mammography acquisition units have already been shown to be equal or better than screen film systems for the detection and classification of breast lesions. The optimal multimodality breast imaging diagnostic workstations and connectivity to existing picture and archiving communication systems and information systems is still a work in progress, but with more and more facilities transitioning to digital imaging it is only a matter of time until these hurdles are overcome.

Rationale and Objectives To compare time to interpretation and diagnostic performance levels during repeat readings of full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) in a retrospective study. Materials and Methods Three experienced radiologists twice interpreted 125 selected examinations, 35 with verified cancers and 90 negative for cancer during a period of 22 months using FFDM alone followed by a combined FFDM + DBT mode. Changes in time to “review and rate” these examinations as well as in diagnostic performance levels where assessed. A fixed-effect analysis accounting for cross-correlation due to the review of the same examinations by the same readers was performed. Results The total (combined) time to review and rate an examination increased on average by 33% between the first and second readings of the same examinations (P Conclusion DBT-based breast imaging may ultimately result in a substantial increase in performance; however, without efficiency improvements DBT may take longer to interpret. Addition of “false-positive recalls” was most strongly associated with increase in interpretation time while elimination of “false-positive recalls” did not require longer interpretation time.

This article outlines the reasons that many radiology practices are converting to digital mammography. In addition, it provides basic information about the issues that must be considered in making the transformation. These issues include technical matters regarding image display, storage, and retrieval as well as clinical and ergonomic considerations.

Background We investigated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement kinetic variables quantified from normal breast parenchyma for association with presence of breast cancer, in a case-control study. Methods Under a Health Insurance Portability and Accountability Act compliant and Institutional Review Board-approved protocol, DCE-MRI scans of the contralateral breasts of 51 patients with cancer and 51 controls (matched by age and year of MRI) with biopsy-proven benign lesions were retrospectively analyzed. Applying fully automated computer algorithms on pre-contrast and multiple post-contrast MR sequences, two contrast enhancement kinetic variables, wash-in slope and signal enhancement ratio, were quantified from normal parenchyma of the contralateral breasts of both patients with cancer and controls. Conditional logistic regression was employed to assess association between these two measures and presence of breast cancer, with adjustment for other imaging factors including mammographic breast density and MRI background parenchymal enhancement (BPE). The area under the receiver operating characteristic curve (AUC) was used to assess the ability of the kinetic measures to distinguish patients with cancer from controls. Results When both kinetic measures were included in conditional logistic regression analysis, the odds ratio for breast cancer was 1.7 (95 % CI 1.1, 2.8; p = 0.017) for wash-in slope variance and 3.5 (95 % CI 1.2, 9.9; p = 0.019) for signal enhancement ratio volume, respectively. These odds ratios were similar on respective univariate analysis, and remained significant after adjustment for menopausal status, family history, and mammographic density. While percent BPE was associated with an odds ratio of 3.1 (95 % CI 1.2, 7.9; p = 0.018), in multivariable analysis of the three measures, percent BPE was non-significant (p = 0.897) and the two kinetics measures remained significant. For the differentiation of patients with cancer and controls, the unadjusted AUC was 0.71 using a combination of the two measures, which significantly (p = 0.005) outperformed either measure alone (AUC = 0.65 for wash-in slope variance and 0.63 for signal enhancement ratio volume). Conclusions Kinetic measures of wash-in slope and signal enhancement ratio quantified from normal parenchyma in DCE-MRI are jointly associated with presence of breast cancer, even after adjustment for mammographic density and BPE.

Breast magnetic resonance imaging (MRI) is recommended as an adjunct to mammography for women who are considered at elevated risk of developing breast cancer. As a key component of breast MRI, dynamic contrast-enhanced MRI (DCE-MRI) uses a contrast agent to provide high intensity contrast between breast tissues, making it sensitive to tissue composition and vascularity. Breast DCE-MRI characterizes certain physiologic properties of breast tissue that are potentially related to breast cancer risk. Studies have shown that increased background parenchymal enhancement (BPE), which is the contrast enhancement occurring in normal cancer-unaffected breast tissues in post-contrast sequences, predicts increased breast cancer risk. Signal enhancement ratio (SER) computed from pre-contrast and post-contrast sequences in DCE-MRI measures change in signal intensity due to contrast uptake over time and is a measure of contrast enhancement kinetics. SER quantified in breast tumor has been shown potential as a biomarker for characterizing tumor response to treatments. In this work we investigated the relationship between quantitative measures of SER and breast cancer risk. A pilot retrospective case-control study was performed using a cohort of 102 women, consisting of 51 women who had diagnosed with unilateral breast cancer and 51 matched controls (by age and MRI date) with a unilateral biopsy-proven benign lesion. SER was quantified using fully-automated computerized algorithms and three SER-derived quantitative volume measures were compared between the cancer cases and controls using logistic regression analysis. Our preliminary results showed that SER is associated with breast cancer risk, after adjustment for the Breast Imaging Reporting and Data System (BI-RADS)-based mammographic breast density measures. This pilot study indicated that SER has potential for use as a risk factor for breast cancer risk assessment in women at elevated risk of developing breast cancer.

This study analyzes mammography screening data from the National Mammography Database to determine the age cutoff point for screening.

To evaluate observer sensitivity to small differences in image presentation, a multipoint rank-order experiment was used to identify small differences or trends in observations.Ten observers were presented with 50 sets of breast images that had been compressed at five different levels. Each set contained six images ranging from noncompressed to approximately 101:1 compression. Observers were asked to review all images of a case side by side and rank order the quality of each to enable determination of the presence or absence of masses and clustered microcalcifications.As a group, observers were able to detect small differences among the images, even at the lower compression levels (p.001). As compression levels and image degradation increased, the ability to identify differences between different modes also increased. Large observer variability in discrimination ability was observed.Multipoint rank ordering of images viewed side by side can be an efficient method to identify small differences in image presentation. This approach to image ranking could be used to rule out or confirm the need for objective observer performance-type studies.

Purpose: To determine associations between radiologist-annotated MRI features and genomic measurements in breast invasive carcinoma (BRCA) from the Cancer Genome Atlas (TCGA). Methods: 98 TCGA patients with BRCA were assessed by a panel of radiologists (TCGA Breast Phenotype Research Group) based on a variety of mass and non-mass features according to the Breast Imaging Reporting and Data System (BI-RADS). Batch corrected gene expression data was obtained from the TCGA Data Portal. The Kruskal-Wallis test was used to assess correlations between categorical image features and tumor-derived genomic features (such as gene pathway activity, copy number and mutation characteristics). Image-derived features were also correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) status. Multiple hypothesis correction was done using Benjamini-Hochberg FDR. Associations at an FDR of 0.1 were selected for interpretation. Results: ER status was associated with rim enhancement and peritumoral edema. PR status was associated with internal enhancement. Several components of the PI3K/Akt pathway were associated with rim enhancement as well as heterogeneity. In addition, several components of cell cycle regulation and cell division were associated with imaging characteristics.TP53 and GATA3 mutations were associated with lesion size. MRI features associated with TP53 mutation status were rim enhancement and peritumoral edema. Rim enhancement was associated with activity of RB1, PIK3R1, MAP3K1, AKT1,PI3K, and PIK3CA. Margin status was associated with HIF1A/ARNT, Ras/ GTP/PI3K, KRAS, and GADD45A. Axillary lymphadenopathy was associated with RB1 and BCL2L1. Peritumoral edema was associated with Aurora A/GADD45A, BCL2L1, CCNE1, and FOXA1. Heterogeneous internal nonmass enhancement was associated with EGFR, PI3K, AKT1, HF/MET, and EGFR/Erbb4/neuregulin 1. Diffuse nonmass enhancement was associated with HGF/MET/MUC20/SHIP, and HGF/MET/RANBP9. Linear nonmass enhancement was associated with PIK3R1 and AKT activity. Conclusion: MRI-genomic association analysis revealed that several BRCA-associated gene features were associated with radiologist-annotated image features.

To assess interpretation performance and radiation dose when two-dimensional synthesized mammography (SM) images versus standard full-field digital mammography (FFDM) images are used alone or in combination with digital breast tomosynthesis images.A fully crossed, mode-balanced multicase (n = 123), multireader (n = 8), retrospective observer performance study was performed by using deidentified images acquired between 2008 and 2011 with institutional review board approved, HIPAA-compliant protocols, during which each patient signed informed consent. The cohort included 36 cases of biopsy-proven cancer, 35 cases of biopsy-proven benign lesions, and 52 normal or benign cases (Breast Imaging Reporting and Data System [BI-RADS] score of 1 or 2) with negative 1-year follow-up results. Accuracy of sequentially reported probability of malignancy ratings and seven-category forced BI-RADS ratings was evaluated by using areas under the receiver operating characteristic curve (AUCs) in the random-reader analysis.Probability of malignancy-based mean AUCs for SM and FFDM images alone was 0.894 and 0.889, respectively (difference, -0.005; 95% confidence interval [CI]: -0.062, 0.054; P = .85). Mean AUC for SM with tomosynthesis and FFDM with tomosynthesis was 0.916 and 0.939, respectively (difference, 0.023; 95% CI: -0.011, 0.057; P = .19). In terms of the reader-specific AUCs, five readers performed better with SM alone versus FFDM alone, and all eight readers performed better with combined FFDM and tomosynthesis (absolute differences from 0.003 to 0.052). Similar results were obtained by using a nonparametric analysis of forced BI-RADS ratings.SM alone or in combination with tomosynthesis is comparable in performance to FFDM alone or in combination with tomosynthesis and may eliminate the need for FFDM as part of a routine clinical study.

To compare the diagnostic performance of breast tomosynthesis versus supplemental mammography views in classification of masses, distortions, and asymmetries.Eight radiologists who specialized in breast imaging retrospectively reviewed 217 consecutively accrued lesions by using protocols that were HIPAA compliant and institutional review board approved in 182 patients aged 31-60 years (mean, 50 years) who underwent diagnostic mammography and tomosynthesis. The lesions in the cohort included 33% (72 of 217) cancers and 67% (145 of 217) benign lesions. Eighty-four percent (182 of 217) of the lesions were masses, 11% (25 of 217) were asymmetries, and 5% (10 of 217) were distortions that were initially detected at clinical examination in 8% (17 of 217), at mammography in 80% (173 of 217), at ultrasonography (US) in 11% (25 of 217), or at magnetic resonance imaging in 1% (2 of 217). Histopathologic examination established truth in 191 lesions, US revealed a cyst in 12 lesions, and 14 lesions had a normal follow-up. Each lesion was interpreted once with tomosynthesis and once with supplemental mammographic views; both modes included the mediolateral oblique and craniocaudal views in a fully crossed and balanced design by using a five-category Breast Imaging Reporting and Data System (BI-RADS) assessment and a probability-of-malignancy score. Differences between modes were analyzed with a generalized linear mixed model for BI-RADS-based sensitivity and specificity and with modified Obuchowski-Rockette approach for probability-of-malignancy-based area under the receiver operating characteristic (ROC) curve.Average probability-of-malignancy-based area under the ROC curve was 0.87 for tomosynthesis versus 0.83 for supplemental views (P.001). With tomosynthesis, the false-positive rate decreased from 85% (989 of 1160) to 74% (864 of 1160) (P.01) for cases that were rated BI-RADS category 3 or higher and from 57% (663 of 1160) to 48% (559 of 1160) for cases rated BI-RADS category 4 or 5 (P.01), without a meaningful change in sensitivity. With tomosynthesis, more cancers were classified as BI-RADS category 5 (39% [226 of 576] vs 33% [188 of 576]; P = .017) without a decrease in specificity.Tomosynthesis significantly improved diagnostic accuracy for noncalcified lesions compared with supplemental mammographic views.

The FDA recently approved Digital Breast Tomosynthesis (DBT) for use in screening for the early detection of breast cancer. However, MQSA qualification for interpreting DBT through training was noted as important. Performance issues related to training are largely unknown. Therefore, we assembled a unique computerized training module to assess radiologists' performances before and after using the training module. Seventy-one actual baseline mammograms (no priors) with FFDM and DBT images were assembled to be read before and after training with the developed module. Fifty examinations of FFDM and DBT images enriched with positive findings were assembled for the training module. Depicted findings were carefully reviewed, summarized, and entered into a specially designed training database where findings were identified by case number and synchronized to the display of the related FFDM plus DBT examinations on a clinical workstation. Readers reported any findings using screening BIRADS (0, 1, or 2) followed by instantaneous feedback of the verified truth. Six radiologists participated in the study and reader average sensitivity and specificity were compared before and after training. Average sensitivity improved and specificity remained relatively the same after training. Performance changes may be affected by disease prevalence in the training set.