Comparison of contrast-enhanced cone-beam breast CT, MRI, and mammography for breast cancer characterization

OBJECTIVES

To compare the consistency of contrast-enhanced cone-beam breast CT (CE-CBBCT), MRI and mammography regarding characterization of breast cancer.

METHODS

In this retrospective study, patients with breast cancer who underwent preoperative CE-CBBCT, MRI, and mammography between January 2017 and July 2022 were enrolled. Three experienced radiologists independently interpreted the characteristics of breast lesions on each imaging mode referring to BI-RADS, with a 4-week wash-out period. One of the three radiologists reviewed the CE-CBBCT images 4 weeks after the initial evaluation. Cross-modality consistency was calculated by Cohen's Kappa based on majority report. Inter-and intra-reader agreement were assessed using Fleiss and Cohen's Kappa, respectively. The association between imaging factors and consistency levels was analyzed using chi-square and Mann-Whitney U test.

RESULTS

A total of 214 malignant lesions identified in 207 patients were enrolled. CE-CBBCT showed almost perfect agreement with MRI on lesion type identification (Kappa = 0.865, 95% CI: 0.802-0.928), but fair agreement with mammography (Kappa = 0.287, 95% CI: 0.205-0.369). CE-CBBCT showed substantial agreement on characterization with MRI for both mass (Kappa = 0.752-0.824) and non-mass enhancement (NME) (Kappa = 0.702-0.729), and non-contrast-enhanced CBBCT (NCE-CBBCT) showed substantial agreement with mammography for calcification (Kappa = 0.717-0.777). Inter- (Kappa = 0.611-0.738) and intra-reader (Kappa = 0.757-0.887) agreement were substantial on CE-CBBCT interpretation. There was no statistically significant difference in imaging factors between different consistency levels (all p > 0.05).

CONCLUSIONS

CE-CBBCT showed high consistency on mass and NME characterization with MRI, and on calcification with mammography, indicating that CE-CBBCT could combine morphology, hemodynamic and calcification features, and the corresponding descriptors have the feasibility to describe CE-CBBCT characteristics of breast cancer.

KEY POINTS

Question Contrast-enhanced cone-beam breast CT (CE-CBBCT) is widely used in the diagnosis and assessment of breast cancer, but there is no standardized lexicon for image interpretation. Findings CE-CBBCT showed high consistency and comparable reproducibility with MRI and mammography for characterizing breast cancer lesions. Clinical relevance The findings prove that CE-CBBCT could combine morphology, hemodynamic, and calcification features and provide support for the feasibility of applying the BI-RADS descriptors of MRI and mammography to interpret contrast-enhanced cone-beam breast CT images in clinic.

Increased background parenchymal enhancement on peri-menopausal breast magnetic resonance imaging

Objectives

To examine the background parenchymal enhancement (BPE) levels in peri-menopausal breast MRI compared with pre- and post-menopausal breast MRI.

Methods

This study included 562 patients (55.8±12.3 years) who underwent contrast-enhanced dynamic breast MRI between 2011 and 2015 for clinical indications. We evaluated the BPE level, amount of fibroglandular tissue (FGT), and social and clinical variables. The inter-reader agreement for the amount of FGT and the BPE level was evaluated using interclass correlation coefficients. Associations between the BPE level and body mass index (BMI), ages of menarche and menopause, childbirth history, number of children, and the amount of FGT were determined using Spearman's correlation coefficients or Mann-Whitney U-test. Pearson's χ2 test was used to assess the difference in the frequency of BPE categories among the age-groups.

Results

The inter-reader agreement was 0.864 for the amount of FGT and 0.840 for the BPE level, both indicating almost perfect agreement. The BPE level showed a weak positive correlation with the amount of FGT (Spearman's ρ=0.271, P<0.001). BPE was not significantly correlated with BMI, childbirth history, number of births, or ages of menarche or menopause. BPE was greater in the peri-menopausal age-group compared with the corresponding pre- and post-menopausal age-groups, both with benign and malignant lesions.

Conclusions

BPE was greater in the peri-menopausal stage than in the pre- and post-menopausal stages. Our results suggest that BPE showed a non-linear decrease with age and that the hormonal disbalance in the peri-menopausal period has a greater effect on the BPE level than was previously assumed.

Background Parenchymal Enhancement: A Comprehensive Update

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

OBJECTIVES

Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI.

METHODS

This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating.

RESULTS

The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE).

CONCLUSION

It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network's predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background.Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described inbreast imaging reporting and data system(BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM.Materials and methods.A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated.Results.The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2-71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements.Conclusion.The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

Association of Breast Cancer Odds with Background Parenchymal Enhancement Quantified Using a Fully Automated Method at MRI: The IMAGINE Study

Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE (rs = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications

Radiologists today play a central role in making diagnostic decisions and labeling images for training and benchmarking artificial intelligence (AI) algorithms. A key concern is low inter-reader reliability (IRR) seen between experts when interpreting challenging cases. While team-based decisions are known to outperform individual decisions, inter-personal biases often creep up in group interactions which limit nondominant participants from expressing true opinions. To overcome the dual problems of low consensus and interpersonal bias, we explored a solution modeled on bee swarms. Two separate cohorts, three board-certified radiologists, (cohort 1), and five radiology residents (cohort 2) collaborated on a digital swarm platform in real time and in a blinded fashion, grading meniscal lesions on knee MR exams. These consensus votes were benchmarked against clinical (arthroscopy) and radiological (senior-most radiologist) standards of reference using Cohen's kappa. The IRR of the consensus votes was then compared to the IRR of the majority and most confident votes of the two cohorts. IRR was also calculated for predictions from a meniscal lesion detecting AI algorithm. The attending cohort saw an improvement of 23% in IRR of swarm votes (k = 0.34) over majority vote (k = 0.11). Similar improvement of 23% in IRR (k = 0.25) in 3-resident swarm votes over majority vote (k = 0.02) was observed. The 5-resident swarm had an even higher improvement of 30% in IRR (k = 0.37) over majority vote (k = 0.07). The swarm consensus votes outperformed individual and majority vote decision in both the radiologists and resident cohorts. The attending and resident swarms also outperformed predictions from a state-of-the-art AI algorithm.

Background Parenchymal Enhancement at Postoperative Surveillance Breast MRI: Association with Future Second Breast Cancer Risk

Background Background parenchymal enhancement (BPE) is a known risk factor for breast cancer. However, studies on the association between BPE and second breast cancer risk are still lacking. Purpose To investigate whether BPE at surveillance breast MRI is associated with subsequent second breast cancer risk in women with a personal history of breast cancer. Materials and Methods A retrospective search of the imaging database of an academic medical center identified consecutive surveillance breast MRI examinations performed between January 2008 and December 2017 in women who underwent surgery for primary breast cancer and had no prior diagnosis of second breast cancer. BPE at surveillance breast MRI was qualitatively assessed using a four-category classification of minimal, mild, moderate, or marked. Future second breast cancer was defined as ipsilateral breast tumor recurrence or contralateral breast cancer diagnosed at least 1 year after each surveillance breast MRI examination. Factors associated with future second breast cancer risk were evaluated using the multivariable Fine-Gray subdistribution hazard model. Results Among the 2668 women (mean age at baseline surveillance breast MRI, 49 years ± 8 [SD]), 109 developed a second breast cancer (49 ipsilateral, 58 contralateral, and two ipsilateral and contralateral) at a median follow-up of 5.8 years. Mild, moderate, or marked BPE at surveillance breast MRI (hazard ratio [HR], 2.1 [95% CI: 1.4, 3.1]; P < .001), young age (<45 years) at initial breast cancer diagnosis (HR, 3.4 [95% CI: 1.7, 6.4]; P < .001), positive results from a BRCA1/2 genetic test (HR, 6.5 [95% CI: 3.5, 12.0]; P < .001), and negative hormone receptor expression in the initial breast cancer (HR, 1.6 [95% CI: 1.1, 2.6]; P = .02) were independently associated with an increased risk of future second breast cancer. Conclusion Background parenchymal enhancement at surveillance breast MRI was associated with future second breast cancer risk in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Niell in this issue.

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

BACKGROUND

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.

PURPOSE

To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.

STUDY TYPE

Retrospective.

POPULATION

Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).

FIELD STRENGTH/SEQUENCE

A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.

ASSESSMENT

Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.

STATISTICAL TESTS

Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).

RESULTS

The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.

DATA CONCLUSION

Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 3.

Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI

OBJECTIVES

To compare the background parenchymal enhancement (BPE) levels on contrast-enhanced cone-beam breast CT (CE-CBBCT) and MRI, evaluate inter-reader reliability, and analyze the relationship between clinical factors and BPE level on CE-CBBCT.

METHODS

In this retrospective study, patients who underwent both CE-CBBCT and MRI were analyzed. BPE levels on CE-CBBCT and MRI were assessed by five specialists independently in random fashion, with a wash-out period of 4 weeks. Weighted kappa was used to analyze the agreement between CE-CBBCT and MRI, and intraclass correlation coefficient (ICC) was used to evaluate the inter-reader reliability for each modality. The association between BPE level on CE-CBBCT and clinical factors was evaluated by univariate and multivariate logistic regression.

RESULTS

A total of 221 patients from January 2017 to April 2021 were enrolled. CE-CBBCT showed substantial agreement (weighted kappa = 0.690) with MRI for BPE evaluation, with good degree of inter-reader reliability on both CE-CBBCT (ICC = 0.712) and MRI (ICC = 0.757). Based on majority reports, BPE levels on CE-CBBCT were lower than MRI (p < 0.001). BPE level on CE-CBBCT was significantly associated with menstrual status (odds ratio, OR = 0.125), breast density (OR = 2.308), and previously treated breast cancer (OR = 0.052) (all p < 0.05). BPE level for premenopausal patients was associated with menstrual cycle, with lower BPE level for the 2^nd week of menstrual cycle (OR = 0.246).

CONCLUSIONS

CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation, indicating that the corresponding BI-RADS lexicons could be used to describe BPE level on CE-CBBCT. The 2^nd week of menstrual cycle timing is suggested as the optimal examination period for CE-CBBCT.

KEY POINTS

• CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation. • Menstrual status, breast density, and previously treated breast cancer were associated with the BPE level on CE-CBBCT images. • The 2^ndweek of the menstrual cycle is suggested as the optimal examination period for CE-CBBCT.

Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review

Within the past decade, background parenchymal enhancement (BPE) and background parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the diagnosis and treatment monitoring of breast cancer. Growing evidence supports the role of breast parenchyma vascularity and metabolic activity as probable risk factors for breast cancer development. Furthermore, in the presence of a newly-diagnosed breast cancer, added clinically-relevant data was surprisingly found in the respective imaging properties of the non-affected contralateral breast. Evaluation of the contralateral BPE and BPU have been found to be especially instrumental in predicting the prognosis of a patient with breast cancer and even anticipating their response to neoadjuvant chemotherapy. Simultaneously, further research has found a link between these two biomarkers, even though they represent different physical properties. The aim of this review is to provide an up to date summary of the current clinical applications of BPE and BPU as breast cancer imaging biomarkers with the hope that it propels their further usage in clinical practice.

Reliability of ultrasound in evaluating the plantar skin and fat pad of the foot in the setting of diabetes

Ultrasound can be used to assess injury and structural changes to the soft-tissue structure of the foot. It may be useful to assess the feet of people with diabetes who are at increased risk of plantar soft-tissue pathological changes. The aim of this study was to determine if ultrasound measurements of plantar soft-tissue thickness and assessments of tissue acoustic characteristics are reliable in people with and without diabetes mellitus. A repeated measures design was used to determine intra-observer reliability for ultrasound measurements of plantar skin and fat pad thickness and intra- and inter-observer reliability of plantar skin and fat pad tissue characterisation assessments made at foot sites which are at risk of tissue injury in people with diabetes. Thickness measurements and tissue characterisation assessments were obtained at the heel and forefoot in both the unloaded and compressed states and included discrete layers of the plantar tissues: skin, microchamber, horizontal fibrous band, macrochamber and total soft-tissue depth. At each site, relative intra-observer reliability was achieved for the measurement of at least one plantar tissue layer. The total soft-tissue thickness measured in the unloaded state (ICC 0.925-0.976) demonstrated intra-observer reliability and is the most sensitive for detecting small change on repeated measures. Intra-observer agreement was demonstrated for tissue characteristic assessments of the skin at the heel (k = 0.70), fat pad at the lateral sesamoid region (k = 0.70) and both skin and fat pad at the second (k = 0.80, k = 0.70 respectively) and third metatarsal heads (k = 0.90, k = 0.79 respectively). However, acceptable inter-observer agreement was not demonstrated for any tissue characteristic assessment, therefore the use of multiple observers should be avoided when making these assessments.

Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US

Background Breast density at mammography is an established risk factor for breast cancer, but it cannot be used to distinguish between glandular and fibrous tissue. Purpose To evaluate the association between the glandular tissue component (GTC) at screening breast US and the risk of future breast cancer in women with dense breasts and the association between the GTC and lobular involution. Materials and Methods Screening breast US examinations performed in women with no prior history of breast cancer and with dense breasts with negative findings from mammography from January 2012 to December 2015 were retrospectively identified. The GTC was reported as being minimal, mild, moderate, or marked at the time of the US examination. In women who had benign breast biopsy results, the degree of lobular involution in normal background tissue was categorized as not present, mild, moderate, or complete. The GTC-related breast cancer risk in women with a cancer diagnosis or follow-up after 6 months was estimated by using Cox proportional hazards regression. Cumulative logistic regression was used to evaluate the association between the GTC and lobular involution. Results Among 8483 women (mean age, 49 years ± 8 [standard deviation]), 137 developed breast cancer over a median follow-up time of 5.3 years. Compared with a minimal or mild GTC, a moderate or marked GTC was associated with an increased cancer risk (hazard ratio, 1.5; 95% CI: 1.05, 2.1; P = .03) after adjusting for age and breast density. The GTC had an inverse association with lobular involution; women with no, mild, or moderate involution had greater odds (odds ratios of 4.9 [95% CI: 1.5, 16.6], 2.6 [95% CI: 0.95, 7.2], and 1.8 [95% CI: 0.7, 4.6], respectively) of a moderate or marked GTC than those with complete involution (P = .004). Conclusion The glandular tissue component was independently associated with the future breast cancer risk in women with dense breasts and reflects the lobular involution. It should be considered for risk stratification during screening breast US. © RSNA, 2021 Online supplemental material is available for this article.

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk

BACKGROUND. Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. OBJECTIVE. Using a semiautomated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs in significantly predicting subsequent breast cancer risk compared with radiologist-assigned BPE category. METHODS. In this retrospective case-control study, we identified 95 women at high risk of breast cancer but without a personal history of breast cancer who underwent breast MRI. Of these women, 19 subsequently developed breast cancer and were included as cases. Each case was age matched to four control patients (76 control patients total). Sociodemographic characteristics were compared between the cases and matched control patients using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI examination, quantitative fibroglandular tissue and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volume above thresholds (BPE volume in cm³), and estimating the percentage of enhancing tissue above thresholds relative to total breast volume (BPE%) on each gadolinium-enhanced phase. For the 91 imaging features generated, we compared predictive performance using conditional logistic regression with 80:20 hold-out cross validation and ROC curve analysis. ROC AUC was the figure of merit. Sensitivity, specificity, PPV, and NPV were also computed. All feature pairs were exhaustively searched to identify those with the highest AUC and Youden index. A DeLong test was used to compare predictive performance (AUCs). RESULTS. Women subsequently diagnosed with breast cancer were more likely to have mild, moderate, or marked BPE (odds ratio, 3.0; 95% CI, 0.9-10.0; p = .07). According to ROC curve analysis, a BPE category threshold greater than minimal resulted in a maximized AUC (0.62) in distinguishing cases from control patients. Compared with BPE category, the first gadolinium-enhanced (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds yielded significantly higher AUC values of 0.85 (p = .0007) and 0.84 (p = .0004), respectively. Feature combinations showed similar AUC values with improved sensitivity. CONCLUSION. Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. CLINICAL IMPACT. Future risk prediction models that incorporate quantitative measures warrant additional investigation.

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Background: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. Method: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). Results: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. Conclusions: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.

Association of breast cancer with MRI background parenchymal enhancement: the IMAGINE case-control study

Background

Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be associated with breast cancer risk, but previous studies of the association are equivocal and limited by incomplete blinding of BPE assessment. In this study, we evaluated the association between BPE and breast cancer based on fully blinded assessments of BPE in the unaffected breast.

Methods

The Imaging and Epidemiology (IMAGINE) study is a multicenter breast cancer case-control study of women receiving diagnostic, screening, or follow-up breast MRI, recruited from three comprehensive cancer centers in the USA. Cases had a first diagnosis of unilateral breast cancer and controls had no history of or current breast cancer. A single board-certified breast radiologist with 12 years’ experience, blinded to case-control status and clinical information, assessed the unaffected breast for BPE without view of the affected breast of cases (or the corresponding breast laterality of controls). The association between BPE and breast cancer was estimated by multivariable logistic regression separately for premenopausal and postmenopausal women.

Results

The analytic dataset included 835 cases and 963 controls. Adjusting for fibroglandular tissue (breast density), age, race/ethnicity, BMI, parity, family history of breast cancer, BRCA1/BRCA2 mutations, and other confounders, moderate/marked BPE (vs minimal/mild BPE) was associated with breast cancer among premenopausal women [odds ratio (OR) 1.49, 95% CI 1.05–2.11; p = 0.02]. Among postmenopausal women, mild/moderate/marked vs minimal BPE had a similar, but statistically non-significant, association with breast cancer (OR 1.45, 95% CI 0.92–2.27; p = 0.1).

Conclusions

BPE is associated with breast cancer in premenopausal women, and possibly postmenopausal women, after adjustment for breast density and confounders. Our results suggest that BPE should be evaluated alongside breast density for inclusion in models predicting breast cancer risk.

Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols

PURPOSE

To propose and evaluate a fully automated technique for quantification of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in breast MRI.

METHODS

We propose a fully automated method, where after preprocessing, FGT is segmented in T1-weighted, nonfat-saturated MRI. Incorporating an anatomy-driven prior probability for FGT and robust texture descriptors against intensity variations, our method effectively addresses major image processing challenges, including wide variations in breast anatomy and FGT appearance among individuals. Our framework then propagates this segmentation to dynamic contrast-enhanced (DCE)-MRI to quantify BPE within the segmented FGT regions. Axial and sagittal image data from 40 cancer-unaffected women were used to evaluate our proposed method vs a manually annotated reference standard.

RESULTS

High spatial correspondence was observed between the automatic and manual FGT segmentation (mean Dice similarity coefficient 81.14%). The FGT and BPE quantifications (denoted FGT% and BPE%) indicated high correlation (Pearson's r = 0.99 for both) between automatic and manual segmentations. Furthermore, the differences between the FGT% and BPE% quantified using automatic and manual segmentations were low (mean differences: -0.66 ± 2.91% for FGT% and -0.17 ± 1.03% for BPE%). When correlated with qualitative clinical BI-RADS ratings, the correlation coefficient for FGT% was still high (Spearman's ρ = 0.92), whereas that for BPE was lower (ρ = 0.65). Our proposed approach also performed significantly better than a previously validated method for sagittal breast MRI.

CONCLUSIONS

Our method demonstrated accurate fully automated quantification of FGT and BPE in both sagittal and axial breast MRI. Our results also suggested the complexity of BPE assessment, demonstrating relatively low correlation between segmentation and clinical rating.

Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models

BACKGROUND

Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE).

PURPOSE

To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification.

STUDY TYPE

Retrospective.

POPULATION

A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set.

FIELD STRENGTH/SEQUENCE

3T and 1.5T; T₂ -weighted, fat-saturated T₁ -weighted (T₁ W) with dynamic contrast enhancement (DCE).

ASSESSMENT

Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre- and postcontrast T₁ W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE.

STATISTICAL TESTS

Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification.

RESULTS

The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods.

DATA CONCLUSION

This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Factors Associated With Background Parenchymal Enhancement on Contrast-Enhanced Mammography

OBJECTIVE. The purpose of this study was to determine the relationship between background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM) and breast tissue density, menstrual status, endocrine therapy, and risk factors for breast cancer and also to evaluate interreader agreement on classification of BPE on CEM. MATERIALS AND METHODS. Five subspecialty-trained breast radiologists independently and blindly graded tissue density (with fatty tissue and scattered fibroglandular tissue classified as nondense tissue and with heterogeneously dense and extremely dense classified as dense tissue) and BPE (with minimal or mild BPE categorized as low BPE and moderate or marked BPE categorized as high BPE) on CEM examinations performed from 2014 to 2018. Electronic medical charts were reviewed for information on menstrual status, endocrine therapy, history of breast surgery, and other risk factors for breast cancer. Comparisons were performed using the Kruskal-Wallis test, Mann-Whitney test, and Spearman rank correlation. Interreader agreement was estimated using the Fleiss kappa test. RESULTS. A total of 202 patients (mean [± SD] age, 54 ± 10 years; range, 25-84 years) underwent CEM. Tissue density was categorized as fatty in two patients (1%), scattered fibroglandular in 67 patients (33%), heterogeneously dense in 117 patients (58%), and extremely dense in 16 patients (8%). Among the 202 patients, BPE was minimal in 77 (38%), mild in 80 (40%), moderate in 31 (15%), and marked in 14 (7%). Dense breasts, younger age, premenopausal status, no history of endocrine therapy, and no history of breast cancer were significantly associated with high BPE. Among premenopausal patients, no association was found between BPE and time from last menstrual period to CEM. Overall interreader agreement on BPE was moderate (κ = 0.41; 95% CI, 0.40-0.42). Interreader agreement on tissue density was substantial (κ = 0.67; 95% CI, 0.66-0.69). CONCLUSION. Women with dense breasts, premenopausal status, and younger age are more likely to have greater BPE. Targeting CEM to the last menstrual period is not indicated.

Measuring intrarater association between correlated ordinal ratings

Variability between raters' ordinal scores is commonly observed in imaging tests, leading to uncertainty in the diagnostic process. In breast cancer screening, a radiologist visually interprets mammograms and MRIs, while skin diseases, Alzheimer's disease, and psychiatric conditions are graded based on clinical judgment. Consequently, studies are often conducted in clinical settings to investigate whether a new training tool can improve the interpretive performance of raters. In such studies, a large group of experts each classify a set of patients' test results on two separate occasions, before and after some form of training with the goal of assessing the impact of training on experts' paired ratings. However, due to the correlated nature of the ordinal ratings, few statistical approaches are available to measure association between raters' paired scores. Existing measures are restricted to assessing association at just one time point for a single screening test. We propose here a novel paired kappa to provide a summary measure of association between many raters' paired ordinal assessments of patients' test results before versus after rater training. Intrarater association also provides valuable insight into the consistency of ratings when raters view a patient's test results on two occasions with no intervention undertaken between viewings. In contrast to existing correlated measures, the proposed kappa is a measure that provides an overall evaluation of the association among multiple raters' scores from two time points and is robust to the underlying disease prevalence. We implement our proposed approach in two recent breast-imaging studies and conduct extensive simulation studies to evaluate properties and performance of our summary measure of association.

Relationship Between Breast Ultrasound Background Echotexture and Magnetic Resonance Imaging Background Parenchymal Enhancement and the Effect of Hormonal Status Thereon

We studied the relationship between breast ultrasound background echotexture (BET) and magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and whether this relationship varied with hormonal status and amount of fibroglandular tissue (FGT) on MRI. Two hundred eighty-three Korean women (52.1 years; range = 27-79 years) with newly diagnosed primary breast cancer who underwent preoperative breast ultrasound and MRI were retrospectively studied. Background echotexture, BPE, and FGT were classified into 4 categories, and age, menopausal status, menstrual cycle regularity, and menstrual cycle stage at MRI were recorded. Background echotexture and BPE relationship was assessed overall, and in menopausal, FGT, menstrual cycle regularity, and menstrual cycle stage subgroups. Background echotexture and BPE correlated in women overall, and menopausal, FGT, and menstrual cycle subgroups and those in the first half of the cycle (all P < 0.001). Background echotexture reflects BPE, regardless of menopausal status, menstrual cycle regularity, and FGT and may be a biomarker of breast cancer risk.

Methods of assessing categorical agreement between correlated screening tests in clinical studies

Advances in breast imaging and other screening tests have prompted studies to evaluate and compare the consistency between experts' ratings of existing with new screening tests. In clinical settings, medical experts make subjective assessments of screening test results such as mammograms. Consistency between experts' ratings is evaluated by measures of inter-rater agreement or association. However, conventional measures, such as Cohen's and Fleiss' kappas, are unable to be applied or may perform poorly when studies consist of many experts, unbalanced data, or dependencies between experts' ratings exist. Here we assess the performance of existing approaches including recently developed summary measures for assessing the agreement between experts' binary and ordinal ratings when patients undergo two screening procedures. Methods to assess consistency between repeated measurements by the same experts are also described. We present applications to three large-scale clinical screening studies. Properties of these agreement measures are illustrated via simulation studies. Generally, a model-based approach provides several advantages over alternative methods including the ability to flexibly incorporate various measurement scales (i.e. binary or ordinal), large numbers of experts and patients, sparse data, and robustness to prevalence of underlying disease.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement

The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospective study evaluated 1114 breast volumes in 137 patients using T1 precontrast, T1 postcontrast, and T1 subtraction images. First, using our previously published method of quantification, we manually segmented and calculated the amount of FGT and BPE to establish ground truth parameters. Then, a novel 3D CNN modified from the standard 2D U-Net architecture was developed and implemented for voxel-wise prediction whole breast and FGT margins. In the collapsing arm of the network, a series of 3D convolutional filters of size 3 × 3 × 3 are applied for standard CNN hierarchical feature extraction. To reduce feature map dimensionality, a 3 × 3 × 3 convolutional filter with stride 2 in all directions is applied; a total of 4 such operations are used. In the expanding arm of the network, a series of convolutional transpose filters of size 3 × 3 × 3 are used to up-sample each intermediate layer. To synthesize features at multiple resolutions, connections are introduced between the collapsing and expanding arms of the network. L2 regularization was implemented to prevent over-fitting. Cases were separated into training (80%) and test sets (20%). Fivefold cross-validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX Titan X GPU. In the test set, the fully automated CNN method for quantifying the amount of FGT yielded accuracy of 0.813 (cross-validation Dice score coefficient) and Pearson correlation of 0.975. For quantifying the amount of BPE, the CNN method yielded accuracy of 0.829 and Pearson correlation of 0.955. Our CNN network was able to quantify FGT and BPE within an average of 0.42 s per MRI case. A fully automated CNN method can be utilized to quantify MRI FGT and BPE. Larger dataset will likely improve our model.

Background parenchymal enhancement assessment: Inter- and intra-rater reliability across breast MRI sequences

OBJECTIVE

To evaluate inter- and intra-rater reliability of background parenchymal enhancement (BPE) assessment across breast MRI sequences.

MATERIALS AND METHODS

Institutional review board approval was obtained and the requirement for consent was waived. Three radiologists qualitatively categorized BPE on 150 breast MRI using a four-point scale (minimal, mild, moderate or marked) according to BI-RADS category system. According to MR-sequence used for the assessment of BPE, inter-rater and intra-rater reliability across a simulated reading strategy with four options was performed: (1) initial contrast-enhanced (CE) fat-suppressed T1-weighted images (2) initial CE subtracted images (3) maximum-intensity-projection (MIP) of the first CE subtracted images (4) combination of initial CE fat-suppressed T1-weighted, initial CE subtracted and MIP images. Raters repeated BPE assessment of 45 breast MRI four weeks after the initial assessment. Gwet's AC1 index with ordinal weights was used to assess reliabilities.

RESULTS

Gwet's index for the reliability among the three raters was 0.68 (0.63-0.74) using initial contrast-enhanced fat-suppressed T1 weighted images, 0.74 (0.69-0.80) using subtracted images, 0.80 (0.76-0.83) using MIP, 0.80 (0.77-0.84) using a combination of the initial contrast-enhanced fat-suppressed T1 weighted, initial contrast-enhanced subtracted and MIP images. Test-retest reliability was 0.81 (0.60-1.00) for rater 1, 0.77 (0.55-0.98) for rater 2, 0.79 (0.59-0.99) for rater 3 using the combination of initial contrast-enhanced fat-suppressed T1 weighted, initial contrast-enhanced subtracted and MIP images.

CONCLUSIONS

Overall, the combination of all CE MRI images showed the highest reliability of BPE assessment. However, MIP showed a high reliability with lower reading time compared to the combination of all CE MRI images.

Population-Based Assessment of the Association Between Magnetic Resonance Imaging Background Parenchymal Enhancement and Future Primary Breast Cancer Risk

PURPOSE

To evaluate comparative associations of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and mammographic breast density with subsequent breast cancer risk.

PATIENTS AND METHODS

We examined women undergoing breast MRI in the Breast Cancer Surveillance Consortium from 2005 to 2015 (with one exam in 2000) using qualitative BPE assessments of minimal, mild, moderate, or marked. Breast density was assessed on mammography performed within 5 years of MRI. Among women diagnosed with breast cancer, the first BPE assessment was included if it was more than 3 months before their first diagnosis. Breast cancer risk associated with BPE was estimated using Cox proportional hazards regression.

RESULTS

Among 4,247 women, 176 developed breast cancer (invasive, n = 129; ductal carcinoma in situ,n = 47) over a median follow-up time of 2.8 years. More women with cancer had mild, moderate, or marked BPE than women without cancer (80% v 66%, respectively). Compared with minimal BPE, increasing BPE levels were associated with significantly increased cancer risk (mild: hazard ratio [HR], 1.80; 95% CI, 1.12 to 2.87; moderate: HR, 2.42; 95% CI, 1.51 to 3.86; and marked: HR, 3.41; 95% CI, 2.05 to 5.66). Compared with women with minimal BPE and almost entirely fatty or scattered fibroglandular breast density, women with mild, moderate, or marked BPE demonstrated elevated cancer risk if they had almost entirely fatty or scattered fibroglandular breast density (HR, 2.30; 95% CI, 1.19 to 4.46) or heterogeneous or extremely dense breasts (HR, 2.61; 95% CI, 1.44 to 4.72), with no significant interaction (P = .82). Combined mild, moderate, and marked BPE demonstrated significantly increased risk of invasive cancer (HR, 2.73; 95% CI, 1.66 to 4.49) but not ductal carcinoma in situ (HR, 1.48; 95% CI, 0.72 to 3.05).

CONCLUSION

BPE is associated with future invasive breast cancer risk independent of breast density. BPE should be considered for risk prediction models for women undergoing breast MRI.

Background parenchymal enhancement on breast MRI: A comprehensive review

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.

Assessment of Quantitative Magnetic Resonance Imaging Background Parenchymal Enhancement Parameters to Improve Determination of Individual Breast Cancer Risk

OBJECTIVES

The aims of this study were to identify optimal quantitative breast magnetic resonance imaging background parenchymal enhancement (BPE) parameters associated with breast cancer risk and compare performance to qualitative assessments.

METHODS

Using a matched case-control cohort of 46 high-risk women who underwent screening magnetic resonance imaging (23 who developed breast cancer matched to 23 who did not), fibroglandular tissue area, BPE area, and intensity metrics (mean, SD, quartiles, skewness, and kurtosis) were quantitatively measured at varying enhancement thresholds. Optimal thresholds for discriminating between cancer and control cohorts were identified for each metric and performance summarized using area under the receiver operating characteristic curve.

RESULTS

Women who developed breast cancer exhibited greater BPE area (adjusted P = 0.004) and higher intensity statistics (adjusted P < 0.004, except skewness and kurtosis with P > 0.99) than did control subjects, with areas under the receiver operating characteristic curve ranging from 0.75 to 0.78 at optimized thresholds.

CONCLUSIONS

Elevated quantitative BPE parameters, related to both area and intensity of enhancement, are associated with breast cancer development.

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk

RATIONALE AND OBJECTIVES

To determine if background parenchymal enhancement (BPE) on screening breast magnetic resonance imaging (MRI) in high-risk women correlates with future cancer.

MATERIALS AND METHODS

All screening breast MRIs (n = 1039) in high-risk women at our institution from August 1, 2004, to July 30, 2013, were identified. Sixty-one patients who subsequently developed breast cancer were matched 1:2 by age and high-risk indication with patients who did not develop breast cancer (n = 122). Five fellowship-trained breast radiologists independently recorded the BPE. The median reader BPE for each case was calculated and compared between the cancer and control cohorts.

RESULTS

Cancer cohort patients were high-risk because of a history of radiation therapy (10%, 6 of 61), high-risk lesion (18%, 11 of 61), or breast cancer (30%, 18 of 61); BRCA mutation (18%, 11 of 61); or family history (25%, 15 of 61). Subsequent malignancies were invasive ductal carcinoma (64%, 39 of 61), ductal carcinoma in situ (30%, 18 of 61) and invasive lobular carcinoma (7%, 4of 61). BPE was significantly higher in the cancer cohort than in the control cohort (P = 0.01). Women with mild, moderate, or marked BPE were 2.5 times more likely to develop breast cancer than women with minimal BPE (odds ratio = 2.5, 95% confidence interval: 1.3-4.8, P = .005). There was fair interreader agreement (κ = 0.39).

CONCLUSIONS

High-risk women with greater than minimal BPE at screening MRI have increased risk of future breast cancer.

Evaluation of association between degree of background parenchymal enhancement on MRI and breast cancer subtype

PURPOSE

Evaluate possible association between BPE and breast cancer tumor type/prognostic markers.

METHODS

IRB approved retrospective study from 1/2010-1/2014 identified 328 patients who had breast MRI and available clinical/pathology data. BPE was categorized according to BI-RADS. The association between BPE and breast cancer molecular subtype/prognostic factors was evaluated.

RESULTS

No significant association was present between high BPE and the following: HER2+ tumors, basal tumors, tumors with axillary nodal disease, high nuclear grade tumors, high Ki-67 index tumors or larger tumors.

CONCLUSION

Patients with high BPE may be at increased risk for breast cancer but not necessarily for those cancer subtypes with a poor prognosis.

Quantitative analysis of background parenchymal enhancement in whole breast on MRI: Influence of menstrual cycle and comparison with a qualitative analysis

OBJECTIVE

We quantitatively analyzed background parenchymal enhancement (BPE) in whole breast according to menstrual cycle and compared it with a qualitative analysis method.

MATERIALS AND METHODS

A data set of breast magnetic resonance imaging (MRI) from 273 breast cancer patients was used. For quantitative analysis, we used semiautomated in-house software with MATLAB. From each voxel of whole breast, the software calculated BPE using following equation: [(signal intensity [SI] at 1 min 30 s after contrast injection - baseline SI)/baseline SI] × 100%.

RESULTS

In total, 53 patients had minimal, 108 mild, 87 moderate, and 25 marked BPE. On quantitative analysis, mean BPE values were 33.1% in the minimal, 42.1% in the mild, 59.1% in the moderate, and 81.9% in the marked BPE group showing significant difference (p = .009 for minimal vs. mild, p < 0.001 for other comparisons). Spearman's correlation test showed that there was strong significant correlation between qualitative and quantitative BPE (r = 0.63, p < 0.001). The mean BPE value was 48.7% for patients in the first week of the menstrual cycle, 43.5% in the second week, 49% in the third week, and 49.4% for those in the fourth week. The difference between the second and fourth weeks was significant (p = .005). Median, 90th percentile, and 10th percentile values were also significantly different between the second and fourth weeks but not different in other comparisons (first vs. second, first vs. third, first vs. fourth, second vs. third, or third vs. fourth).

CONCLUSION

Quantitative analysis of BPE correlated well with the qualitative BPE grade. Quantitative BPE values were lowest in the second week and highest in the fourth week.

Background echotexture classification in breast ultrasound: inter-observer agreement study

Background According to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS), background echotexture in breast ultrasound (US) can be categorized as homogeneous or heterogeneous. Purpose To prospectively evaluate the inter-observer agreement of a four-category classification in background echotexture assessments of breast US and to determine whether background echotexture is related to breast cancer risk factors, including mammography density. Material and Methods Thirty-eight healthy women (age range, 25-72) were recruited. Eleven radiologists performed breast US on all participants and classified each background echotexture into four categories (homogeneous, mild, moderate, and marked heterogeneous). The inter-observer agreement in the assessments was measured using kappa statistics (к). The association between background echotexture and breast cancer risk factors, including mammographic density, menopausal status, and parity, were evaluated using Spearman's correlation coefficient (ρ) and multiple linear regression analysis. Results There was moderate inter-observer agreement between the radiologists for the four categories of background echotexture (average к = 0.45). Heterogeneity of the background echotexture was positively correlated with mammographic density in both pre- and postmenopausal women (premenopausal, ρ = 0.42, P < 0.0001; postmenopausal, ρ = 0.56, P < 0.0001). Multiple linear regression analysis revealed that mammographic density and parity were significantly associated with background echotexture. Conclusion Background echotexture assessment of breast US using a four-category classification showed moderate inter-observer agreement, and more heterogeneous background echotexture was associated with denser breasts and lower parity.

Comparison between qualitative and quantitative assessment of background parenchymal enhancement on breast MRI

BACKGROUND

Potential clinical implications of the level of background parenchymal enhancement (BPE) on breast MRI are increasing. Currently, BPE is typically evaluated subjectively. Tests of concordance between subjective BPE assessment and computer-assisted quantified BPE have not been reported.

PURPOSE OR HYPOTHESIS

To compare subjective radiologist assessment of BPE with objective quantified parenchymal enhancement (QPE).

STUDY TYPE

Cross-sectional observational study.

POPULATION

Between 7/24/2015 and 11/27/2015, 104 sequential patients (ages 23 - 81 years, mean 49 years) without breast cancer underwent breast MRI and were included in this study.

FIELD STRENGTH/SEQUENCE

3T; fat suppressed axial T2, axial T1, and axial fat suppressed T1 before and after intravenous contrast.

ASSESSMENT

Four breast imagers graded BPE at 90 and 180 s after contrast injection on a 4-point scale (a-d). Fibroglandular tissue masks were generated using a phantom-validated segmentation algorithm, and were co-registered to pre- and postcontrast fat suppressed images to define the region of interest. QPE was calculated.

STATISTICAL TESTS

Receiver operating characteristic (ROC) analyses and kappa coefficients (k) were used to compare subjective BPE with QPE.

RESULTS

ROC analyses indicated that subjective BPE at 90 s was best predicted by quantified QPE ≤20.2 = a, 20.3-25.2 = b, 25.3-50.0 = c, >50.0 = d, and at 180 s by quantified QPE ≤ 32.2 = a, 32.3-38.3 = b, 38.4-74.5 = c, >74.5 = d. Agreement between subjective BPE and QPE was slight to fair at 90 s (k = 0.20-0.36) and 180 s (k = 0.19-0.28). At higher levels of QPE, agreement between subjective BPE and QPE significantly decreased for all four radiologists at 90 s (P ≤ 0.004) and for three of four radiologists at 180 s (P ≤ 0.004).

DATA CONCLUSION

Radiologists were less consistent with QPE as QPE increased.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1685-1691.

Effect of Background Parenchymal Enhancement on Breast MR Imaging Interpretive Performance in Community-based Practices

Purpose To evaluate the effect of background parenchymal enhancement (BPE) on breast magnetic resonance (MR) imaging interpretive performance in a large multi-institutional cohort with independent analysis of screening and diagnostic MR studies. Materials and Methods Analysis of 3770 breast MR studies was conducted. Examinations were performed in 2958 women at six participating facilities in the San Francisco Bay Area from January 2010 to October 2012. Findings were recorded prospectively in the San Francisco Mammography Registry. Performance measures were compared between studies with low BPE (mild or minimal) and those with high BPE (moderate or marked) by using binomial tests of proportions. Results Of 1726 MR imaging studies in the screening group, 1301 were classified as having low BPE and 425 were classified as having high BPE (75% vs 25%, respectively; P < .001). Of 2044 MR imaging studies in the diagnostic group, 1443 were classified as having low BPE and 601 were classified as having high BPE (71% vs 29%, respectively; P < .001). For low versus high BPE groups at screening, abnormal interpretation rate was 157 of 1301 versus 111 of 424 (12% vs 26%, P < .001); biopsy recommendation rate was 85 of 1301 versus 54 of 424 (7% vs 13%, P < .001); and specificity was 89% (95% confidence interval [CI]: 87, 91) versus 75% (95% CI: 71, 80) (P = .01). For the low versus high BPE groups at diagnostic MR imaging, biopsy recommendation rate was 325 of 1443 versus 195 of 601 (23% vs 32%, P < .001); and specificity was 86% (95% CI: 84, 88) versus 75% (95% CI: 74, 82) (P < .001). There were no significant differences between studies with low versus high BPE in sensitivity for screening (76% [95% CI: 55, 91] vs 83% [95% CI: 52, 98]; P = .94) or diagnostic (93% [95% CI: 87, 97] vs 96% [95% CI: 87, 99]; P = .69) MR imaging, nor were there significant differences in cancer detection rate per 1000 patients between the low BPE versus high BPE groups for screening (15 per 1000 vs 24 per 1000, P = .30) or diagnostic (78 per 1000 vs 85 per 1000, P = .64) MR imaging. Conclusion Relative to MR studies with minimal or mild BPE, those with moderate or marked BPE were associated with higher abnormal interpretation and biopsy rates and lower specificity, with no difference in cancer detection rate. ^© RSNA, 2017 Online supplemental material is available for this article.

MRI Texture Analysis of Background Parenchymal Enhancement of the Breast

Purpose

The purpose of this study was to determine texture parameters reflecting the background parenchymal enhancement (BPE) of the breast, which were acquired using texture analysis (TA).

Methods

We investigated 52 breasts of the 26 subjects who underwent dynamic contrast-enhanced MRI. One experienced reader scored BPE visually (i.e., minimal, mild, moderate, and marked). TA, including 12 texture parameters, was performed to distinguish the BPE scores quantitatively. Relationships between the visual BPE scores and texture parameters were evaluated using analysis of variance and receiver operating characteristic analysis.

Results

The variance and skewness of signal intensity were useful for differentiating between moderate and mild or minimal BPE or between mild and minimal BPE, respectively, with the cutoff value of 356.7 for variance and that of 0.21 for skewness. Some TA features could be useful for defining breast lesions from the BPE.

Conclusion

TA may be useful for quantifying the BPE of the breast.

Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis

PURPOSE

To investigate whether the degree of breast magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) is associated with the amount of breast metabolic activity measured by breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography / computed tomography (PET/CT).

MATERIALS AND METHODS

An Institutional Review Board (IRB)-approved retrospective study was performed. Of 327 patients who underwent preoperative breast MRI from 1/1/12 to 12/31/15, 73 patients had 18F-FDG PET/CT evaluation performed within 1 week of breast MRI and no suspicious findings in the contralateral breast. MRI was performed on a 1.5T or 3.0T system. The imaging sequence included a triplane localizing sequence followed by sagittal fat-suppressed T₂ -weighted sequence, and a bilateral sagittal T₁ -weighted fat-suppressed fast spoiled gradient-echo sequence, which was performed before and three times after a rapid bolus injection (gadobenate dimeglumine, Multihance; Bracco Imaging; 0.1 mmol/kg) delivered through an IV catheter. The unaffected contralateral breast in these 73 patients underwent BPE and BPU assessments. For PET/CT BPU calculation, a 3D region of interest (ROI) was drawn around the glandular breast tissue and the maximum standardized uptake value (SUV_max ) was determined. Qualitative MRI BPE assessments were performed on a 4-point scale, in accordance with BI-RADS categories. Additional 3D quantitative MRI BPE analysis was performed using a previously published in-house technique. Spearman's correlation test and linear regression analysis was performed (SPSS, v. 24).

RESULT

The median time interval between breast MRI and 18F-FDG PET/CT evaluation was 3 days (range, 0-6 days). BPU SUV_max mean value was 1.6 (SD, 0.53). Minimum and maximum BPU SUV_max values were 0.71 and 4.0. The BPU SUV_max values significantly correlated with both the qualitative and quantitative measurements of BPE, respectively (r(71) = 0.59, P < 0.001 and r(71) = 0.54, P < 0.001). Qualitatively assessed high BPE group (BI-RADS 3/4) had significantly higher BPU SUV_max of 1.9 (SD = 0.44) compared to low BPE group (BI-RADS 1/2) with an average BPU SUV_max of 1.17 (SD = 0.32) (P < 0.001). On linear regression analysis, BPU SUV_max significantly predicted qualitative and quantitative measurements of BPE (β = 1.29, t(71) = 3.88, P < 0.001 and β = 19.52, t(71) = 3.88, P < 0.001).

CONCLUSION

There is a significant association between breast BPU and BPE, measured both qualitatively and quantitatively. Increased breast cancer risk in patients with high MRI BPE could be due to elevated basal metabolic activity of the normal breast tissue, which may provide a susceptible environment for tumor growth.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:753-759.

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence

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.

Evaluation of background parenchymal enhancement on breast MRI: a systematic review

OBJECTIVE

To perform a systematic review of the methods used for background parenchymal enhancement (BPE) evaluation on breast MRI.

METHODS

Studies dealing with BPE assessment on breast MRI were retrieved from major medical libraries independently by four reviewers up to 6 October 2015. The keywords used for database searching are "background parenchymal enhancement", "parenchymal enhancement", "MRI" and "breast". The studies were included if qualitative and/or quantitative methods for BPE assessment were described.

RESULTS

Of the 420 studies identified, a total of 52 articles were included in the systematic review. 28 studies performed only a qualitative assessment of BPE, 13 studies performed only a quantitative assessment and 11 studies performed both qualitative and quantitative assessments. A wide heterogeneity was found in the MRI sequences and in the quantitative methods used for BPE assessment.

CONCLUSION

A wide variability exists in the quantitative evaluation of BPE on breast MRI. More studies focused on a reliable and comparable method for quantitative BPE assessment are needed. Advances in knowledge: More studies focused on a quantitative BPE assessment are needed.

Comparison of Background Parenchymal Enhancement at Contrast-enhanced Spectral Mammography and Breast MR Imaging

Purpose To assess the extent of background parenchymal enhancement (BPE) at contrast material-enhanced (CE) spectral mammography and breast magnetic resonance (MR) imaging, to evaluate interreader agreement in BPE assessment, and to examine the relationships between clinical factors and BPE. Materials and Methods This was a retrospective, institutional review board-approved, HIPAA-compliant study. Two hundred seventy-eight women from 25 to 76 years of age with increased breast cancer risk who underwent CE spectral mammography and MR imaging for screening or staging from 2010 through 2014 were included. Three readers independently rated BPE on CE spectral mammographic and MR images with the ordinal scale: minimal, mild, moderate, or marked. To assess pairwise agreement between BPE levels on CE spectral mammographic and MR images and among readers, weighted κ coefficients with quadratic weights were calculated. For overall agreement, mean κ values and bootstrapped 95% confidence intervals were calculated. The univariate and multivariate associations between BPE and clinical factors were examined by using generalized estimating equations separately for CE spectral mammography and MR imaging. Results Most women had minimal or mild BPE at both CE spectral mammography (68%-76%) and MR imaging (69%-76%). Between CE spectral mammography and MR imaging, the intrareader agreement ranged from moderate to substantial (κ = 0.55-0.67). Overall agreement on BPE levels between CE spectral mammography and MR imaging and among readers was substantial (κ = 0.66; 95% confidence interval: 0.61, 0.70). With both modalities, BPE demonstrated significant association with menopausal status, prior breast radiation therapy, hormonal treatment, breast density on CE spectral mammographic images, and amount of fibroglandular tissue on MR images (P < .001 for all). Conclusion There was substantial agreement between readers for BPE detected on CE spectral mammographic and MR images. ^© RSNA, 2016.

Quantitative Assessment of Breast Parenchymal Uptake on 18F-FDG PET/CT: Correlation with Age, Background Parenchymal Enhancement, and Amount of Fibroglandular Tissue on MRI

Background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) assessed with MRI have been implicated as sensitive imaging biomarkers for breast cancer. The purpose of this study was to quantitatively assess breast parenchymal uptake (BPU) on ¹⁸F-FDG PET/CT as another valuable imaging biomarker and examine its correlation with BPE, FGT, and age.

METHODS

This study included 129 patients with suspected breast cancer and normal imaging findings in one breast (BI-RADS 1), whose cases were retrospectively analyzed. All patients underwent prone ¹⁸F-FDG PET/CT and 3-T contrast-enhanced MRI of the breast. In all patients, interpreter 1 assessed BPU quantitatively using SUV_max Interpreters 1 and 2 assessed amount of FGT and BPE in the normal contralateral breast by subjective visual estimation, as recommended by BI-RADS. Interpreter 1 reassessed all cases and repeated the BPU measurements. Statistical tests were used to assess correlations between BPU, BPE, FGT, and age, as well as inter- and intrainterpreter agreement.

RESULTS

BPU on ¹⁸F-FDG PET/CT varied among patients. The mean BPU SUV_max ± SD was 1.57 ± 0.6 for patients with minimal BPE, 1.93 ± 0.6 for mild BPE, 2.42 ± 0.5 for moderate BPE, and 1.45 ± 0.3 for marked BPE. There were significant (P < 0.001) moderate to strong correlations among BPU, BPE, and FGT. BPU directly correlated with both BPE and FGT on MRI. Patient age showed a moderate to strong indirect correlation with all 3 imaging-derived tissue biomarkers. The coefficient of variation for quantitative BPU measurements with SUV_max was 5.6%, indicating a high reproducibility. Interinterpreter and intrainterpreter agreement for BPE and FGT was almost perfect, with a κ-value of 0.860 and 0.822, respectively.

CONCLUSION

The results of our study demonstrate that BPU varied among patients. BPU directly correlated with both BPE and FGT on MRI, and BPU measurements were highly reproducible. Patient age showed a strong inverse correlation with all 3 imaging-derived tissue biomarkers. These findings indicate that BPU may serve as a sensitive imaging biomarker for breast cancer prediction, prognosis, and risk assessment.

Three-Dimensional Quantitative Validation of Breast Magnetic Resonance Imaging Background Parenchymal Enhancement Assessments

The magnetic resonance imaging (MRI) background parenchymal enhancement (BPE) and its clinical significance as a biomarker of breast cancer risk has been proposed based on qualitative studies. Previous BPE quantification studies lack appropriate correlation with BPE qualitative assessments. The purpose of this study is to validate our three-dimensional BPE quantification method with standardized BPE qualitative cases. An Institutional Review Board-approved study reviewed 500 consecutive magnetic resonance imaging cases (from January 2013-December 2014) using a strict inclusion criteria and 120 cases that best represented each of the BPE qualitative categories (minimal or mild or moderate or marked) were selected. Blinded to the qualitative data, fibroglandular tissue contours of precontrast and postcontrast images were delineated using an in-house, proprietary segmentation algorithm. Metrics of BPE were calculated including %BPE ([ratio of BPE volume to fibroglandular tissue volume] × 100) at multiple threshold levels to determine the optimal cutoff point for BPE quantification that best correlated with the reference BPE qualitative cases. The highest positive correlation was present at ×1.5 precontrast average signal intensity threshold level (r = 0.84, P < 0.001). At this level, the BPE qualitative assessment of minimal, mild, moderate, and marked correlated with the mean quantitative %BPE of 14.1% (95% CI: 10.9-17.2), 26.1% (95% CI: 22.8-29.3), 45.9% (95% CI: 40.2-51.7), and 74.0% (95% CI: 68.6-79.5), respectively. A one-way analysis of variance with post-hoc analysis showed that at ×1.5 precontrast average signal intensity level, the quantitative %BPE measurements best differentiated the four reference BPE qualitative groups (F [3,117] = 106.8, P < 0.001). Our three-dimensional BPE quantification methodology was validated using the reference BPE qualitative cases and could become an invaluable clinical tool to more accurately assess breast cancer risk and to test chemoprevention strategies.

Breast Background Parenchymal Enhancement on Screening Magnetic Resonance Imaging in Women Who Received Chest Radiotherapy for Childhood Hodgkin's Lymphoma

RATIONALE AND OBJECTIVES

Breast magnetic resonance imaging (MRI) is recommended for the screening of women with a history of chest radiotherapy and consequent increased breast cancer risk. The purpose of this study was to evaluate the impact of prior chest radiotherapy on breast tissue background parenchymal enhancement (BPE) at screening breast MRI.

MATERIALS AND METHODS

A departmental database was reviewed to identify asymptomatic women with either a history of chest radiotherapy for Hodgkin's lymphoma or age-matched controls who underwent screening breast MRI between 2009 and 2013. MRI studies were analyzed on an automated breast MRI viewing platform to calculate breast BPE and breast density.

RESULTS

A total of 61 cases (mean age 41.6 ± 6.75 years) and 61 controls (mean age 40.8 ± 6.99 years) were included. The age of patients at the time of chest radiotherapy was 22.6 ± 8.17 years. Screening MRI was performed 19.0 ± 7.43 years after chest radiotherapy. BPE was significantly higher in patients who received chest radiotherapy (50% vs. 37%, P <0.01). A weak to moderate positive correlation (r > 0.3; P < 0.03) was found between BPE and number of years post radiotherapy. There was a trend toward significant difference between the two groups in the correlation of BPE and age (P = 0.05). Breast density was not significantly different between the two groups.

CONCLUSIONS

BPE is significantly greater in women who receive chest radiotherapy for childhood Hodgkin's lymphoma, and unexpectedly, it positively correlates with the number of years passed after radiation therapy. Long-term biological effects of radiation therapy on breast parenchyma need further research.

Dose-response effects of aerobic exercise on estrogen among women at high risk for breast cancer: a randomized controlled trial

Medical and surgical interventions for elevated breast cancer risk (e.g., BRCA1/2 mutation, family history) focus on reducing estrogen exposure. Women at elevated risk may be interested in less aggressive approaches to risk reduction. For example, exercise might reduce estrogen, yet has fewer serious side effects and less negative impact than surgery or hormonal medications. Randomized controlled trial. Increased risk defined by risk prediction models or BRCA mutation status. Eligibility: Age 18-50, eumenorrheic, non-smokers, and body mass index (BMI) between 21 and 50 kg/m(2). 139 were randomized. Treadmill exercise: 150 or 300 min/week, five menstrual cycles. Control group maintained exercise <75 min/week.

PRIMARY OUTCOME

Area under curve (AUC) for urinary estrogen. Secondary measures: urinary progesterone, quantitative digitized breast dynamic contrast-enhanced magnetic resonance imaging background parenchymal enhancement. Mean age 34 years, mean BMI 26.8 kg/m(2). A linear dose-response relationship was observed such that every 100 min of exercise is associated with 3.6 % lower follicular phase estrogen AUC (linear trend test, p = 0.03). No changes in luteal phase estrogen or progesterone levels. There was also a dose-response effect noted: for every 100 min of exercise, there was a 9.7 % decrease in background parenchymal enhancement as measured by imaging (linear trend test, p = 0.009). Linear dose-response effect observed to reduce follicular phase estrogen exposure measured via urine and hormone sensitive breast tissue as measured by imaging. Future research should explore maintenance of effects and extent to which findings are repeatable in lower risk women. Given the high benefit to risk ratio, clinicians can inform young women at increased risk that exercise may blunt estrogen exposure while considering whether to try other preventive therapies.

Quantitative evaluation of background parenchymal enhancement (BPE) on breast MRI. A feasibility study with a semi-automatic and automatic software compared to observer-based scores

OBJECTIVE

To evaluate quantitative measurements of background parenchymal enhancement (BPE) on breast MRI and compare them with observer-based scores.

METHODS

BPE of 48 patients (mean age: 48 years; age range: 36-66 years) referred to 3.0-T breast MRI between 2012 and 2014 was evaluated independently and blindly to each other by two radiologists. BPE was estimated qualitatively with the standard Breast Imaging Reporting and Data System (BI-RADS) scale and quantitatively with a semi-automatic and an automatic software interface. To assess intrareader agreement, MRIs were re-read after a 4-month interval by the same two readers. The Pearson correlation coefficient (r) and the Bland-Altman method were used to compare the methods used to estimate BPE. p-value <0.05 was considered significant.

RESULTS

The mean value of BPE with the semi-automatic software evaluated by each reader was 14% (range: 2-79%) for Reader 1 and 16% (range: 1-61%) for Reader 2 (p > 0.05). Mean values of BPE percentages for the automatic software were 17.5 ± 13.1 (p > 0.05 vs semi-automatic). The automatic software was unable to produce BPE values for 2 of 48 (4%) patients. With BI-RADS, interreader and intrareader values were κ = 0.70 [95% confidence interval (CI) 0.49-0.91] and κ = 0.69 (95% CI 0.46-0.93), respectively. With semi-automated software, interreader and intrareader values were κ = 0.81 (95% CI 0.59-0.99) and κ = 0.85 (95% CI 0.43-0.99), respectively. BI-RADS scores correlated with the automatic (r = 0.55, p < 0.001) and semi-automatic scores (r = 0.60, p < 0.001). Automatic scores correlated with the semi-automatic scores (r = 0.77, p < 0.001). The mean percentage difference between automatic and semi-automatic scores was 3.5% (95% CI 1.5-5.2).

CONCLUSION

BPE quantitative evaluation is feasible with both semi-automatic and automatic software and correlates with radiologists' estimation.

ADVANCES IN KNOWLEDGE

Computerized BPE quantitative evaluation is feasible with both semi-automatic and automatic software. Computerized BPE quantitative scores correlate with radiologists' estimation.

The relationship of breast density in mammography and magnetic resonance imaging in high-risk women and women with breast cancer

PURPOSE

To evaluate the relationship between mammographic breast density (MBD), background parenchymal enhancement (BPE), and fibroglandular tissue (FGT) in women with breast cancer (BC) and at high risk for developing BC.

METHODS

Our institutional database was queried for patients who underwent mammography and MRI.

RESULTS

Four hundred three (85%) had BC and 72 (15%) were at high risk. MBD (P=.0005), BPE (P<.0001), and FGT (P=.02) were all higher in high-risk women compared to the BC group.

CONCLUSIONS

Higher levels of MBD, BPE and FGT are seen in women at higher risk for developing BC when compared to women with BC.