Background Parenchymal Enhancement Assessment Research Articles

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy -means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, ). Scores from all breast regions performed significantly better than guessing ( from the -test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI

ObjectivesTo compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI.Materials and methodsThis study included screening MRI of 535 women divided into three groups based on lifetime risk: nonhigh-risk women, high-risk women without BRCA mutation, and BRCA1/2 mutation carriers. Six quantitative BPE measurements, including percent enhancement (PE) and signal enhancement ratio (SER), were calculated on DCE-MRI after segmentation of the whole breast and fibroglandular tissue (FGT). The associations between lifetime risk factors and BPE were analyzed via linear regression analysis. We adjusted for risk factors influencing BPE using propensity score matching (PSM) and compared the BPE between different groups. A two-sided Mann–Whitney U-test was used to compare the BPE with a threshold of 0.1 for multiple testing issue-adjusted p values.ResultsAge, BMI, menopausal status, and FGT level were significantly correlated with quantitative BPE based on the univariate and multivariable linear regression analyses. After adjusting for age, BMI, menopausal status, hormonal treatment history, and FGT level using PSM, significant differences were observed between high-risk non-BRCA and BRCA groups in PEFGT (11.5 vs. 8.0%, adjusted p = 0.018) and SERFGT (7.2 vs. 9.3%, adjusted p = 0.066).ConclusionQuantitative BPE varies in women with different lifetime breast cancer risks and BRCA mutation status. These differences may be due to the influence of multiple lifetime risk factors. Quantitative BPE differences remained between groups with and without BRCA mutations after adjusting for known risk factors associated with BPE.Clinical relevance statementBRCA germline mutations may be associated with quantitative background parenchymal enhancement, excluding the effects of known confounding factors. This finding can provide potential insights into the cancer pathophysiological mechanisms behind lifetime risk models.Key PointsExpanding understanding of breast cancer pathophysiology allows for improved risk stratification and optimized screening protocols.Quantitative BPE is significantly associated with lifetime risk factors and differs between BRCA mutation carriers and noncarriers.This research offers a possible understanding of the physiological mechanisms underlying quantitative BPE and BRCA germline mutations.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk.

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI

ObjectivesDevelopment of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification.MethodsFor the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed.ResultsTo assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006).ConclusionsGeneralizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels’ intensity distribution and morphology are required.Critical relevance statementA standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels’ intensity distribution and morphology, an important factor.Key points• Our work contributes to the standardization of FGT and BPE assessment.• Attention U-Net can reliably segment intricately shaped FGT and BPE structures.• The developed models were robust to domain shift.Graphical

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification.

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear. To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status. Retrospective. 954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88). 3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images. Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance. Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result. In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35. Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women. 3 TECHNICAL EFFICACY: Stage 3.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women.

To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM). Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk. During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001). Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females. • Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

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

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. To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. Retrospective. Consecutive high-risk patients (i.e. &gt;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). A 1.5 T or 3T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. 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. 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). 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. Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. 4 TECHNICAL EFFICACY STAGE: 3.

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

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

The Association of Background Parenchymal Enhancement at Breast MRI with Breast Cancer: A Systematic Review and Meta-Analysis

BackgroundThe higher level of background parenchymal enhancement (BPE) at breast MRI has the potential for early detection and prediction of the risk of breast cancer. However, conflicting findings have been reported about the association between the level of BPE at breast MRI and the presence of breast cancer.PurposeTo evaluate the association between qualitative and quantitative BPE at dynamic contrast material-enhanced MRI and breast cancer among populations with average risk and high risk separately.Materials and MethodsA retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of BPE in women with and women without breast cancer was performed for studies published through July 2018. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using DerSimonian-Laird random-effects models. The heterogeneity across the studies was measured by using the statistic I 2. Sensitivity analyses were conducted to test this association according to different study characteristics. P values less than or equal to 5% were considered to indicate statistically significant results.ResultsEighteen studies comprising 1910 women with breast cancer and 2541 control participants were included in the analysis. Among women with high risk, at least moderate BPE (OR, 1.6; 95% CI: 1.0, 2.6; P = .04) or at least mild BPE (OR, 2.1; 95% CI: 1.5, 3.0; P < .001) was associated with higher odds of breast cancer. Furthermore, women with breast cancer showed a higher average BPE percentage compared with control participants with high risk (standardized mean difference, 0.5; 95% CI: 0.2, 0.9; P = .001). No association was observed between at least mild BPE level (P = .15) or at least moderate BPE level (P = .38) and the presence of breast cancer among the population with average risk.ConclusionA higher level of background parenchymal enhancement measured at breast MRI is associated with the presence of breast cancer in women with high risk, but not in women with average risk.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Mann and Pinker in this issue.

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

ObjectiveTo evaluate inter- and intra-rater reliability of background parenchymal enhancement (BPE) assessment across breast MRI sequences. Materials and methodsInstitutional 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. ResultsGwet’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. ConclusionsOverall, 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.

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is challenging, and recent work has demonstrated that convolutional neural networks perform well for this task. In this study, we have investigated the performance of several two-dimensional (2D) U-Net and three-dimensional (3D) U-Net configurations using both fat-suppressed and nonfat-suppressed images. We have also assessed the effect of changing the number and quality of the ground truth segmentations. We designed eight studies to investigate the effect of input types and the dimensionality of the U-Net operations for the breast MRI segmentation. Our training data contained 70 whole breast volumes of T1-weighted sequences without fat suppression (WOFS) and with fat suppression (FS). For each subject, we registered the WOFS and FS volumes together before manually segmenting the breast to generate ground truth. We compared four different input types to the U-nets: WOFS, FS, MIXED (WOFS and FS images treated as separate samples), and MULTI (WOFS and FS images combined into a single multichannel image). We trained 2D U-Nets and 3D U-Nets with these data, which resulted in our eight studies (2D-WOFS, 3D-WOFS, 2D-FS, 3D-FS, 2D-MIXED, 3D-MIXED, 2D-MULTI, and 3D-MULT). For each of these studies, we performed a systematic grid search to tune the hyperparameters of the U-Nets. A separate validation set with 15 whole breast volumes was used for hyperparameter tuning. We performed Kruskal-Walis test on the results of our hyperparameter tuning and did not find a statistically significant difference in the ten top models of each study. For this reason, we chose the best model as the model with the highest mean dice similarity coefficient (DSC) value on the validation set. The reported test results are the results of the top model of each study on our test set which contained 19 whole breast volumes annotated by three readers fused with the STAPLE algorithm. We also investigated the effect of the quality of the training annotations and the number of training samples for this task. The study with the highest average DSC result was 3D-MULTI with 0.96±0.02. The second highest average is 2D WOFS (0.96±0.03), and the third is 2D MULTI (0.96±0.03). We performed the Kruskal-Wallis one-way ANOVA test with Dunn's multiple comparison tests using Bonferroni P-value correction on the results of the selected model of each study and found that 3D-MULTI, 2D-MULTI, 3D-WOFS, 2D-WOFS, 2D-FS, and 3D-FS were not statistically different in their distributions, which indicates that comparable results could be obtained in fat-suppressed and nonfat-suppressed volumes and that there is no significant difference between the 3D and 2D approach. Our results also suggested that the networks trained on single sequence images or multiple sequence images organized in multichannel images perform better than the models trained on a mixture of volumes from different sequences. Our investigation of the size of the training set revealed that training a U-Net in this domain only requires a modest amount of training data and results obtained with 49 and 70 training datasets were not significantly different. To summarize, we investigated the use of 2D U-Nets and 3D U-Nets for breast volume segmentation in T1 fat-suppressed and without fat-suppressed volumes. Although our highest score was obtained in the 3D MULTI study, when we took advantage of information in both fat-suppressed and nonfat-suppressed volumes and their 3D structure, all of the methods we explored gave accurate segmentations with an average DSC on >94% demonstrating that the U-Net is a robust segmentation method for breast MRI volumes.

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development. To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer. Case-control study. In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years. 5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences. Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available. Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared. The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers. Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI. 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

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

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

The Assessment of Background Parenchymal Enhancement (BPE) in a High-Risk Population: What Causes BPE?

OBJECTIVE: To investigate promoting factors for background parenchymal enhancement (BPE) in MR mammography (MRM). METHODS: 146 patients were retrospectively evaluated, including 91 high-risk patients (50 BRCA patients, 41 patients with elevated lifetime risk). 56 screening patients were matched to the high-risk cases on the basis of age. The correlation of BPE with factors such as fibroglandular tissue (FGT), age, menopausal status, breast cancer, high-risk precondition as well as motion were investigated using linear regression. RESULTS: BPE positively correlated with FGT (P<.001) and negatively correlated with menopausal status (P<.001). Cancer did not show an effect on BPE (P>.05). A high-risk precondition showed a significant impact on the formation of BPE (P<.05). However, when corrected for motion, the correlation between BPE and a high-risk precondition became weak and insignificant, and a highly significant association between BPE and motion was revealed (P<.01). CONCLUSION: BPE positively correlated with FGT and negatively correlated with age. Cancer did not have an effect on BPE. A high-risk precondition appears to have a negative effect on BPE. However, when corrected for motion, high-risk preconditions became insignificant. Technical as well as physiological influences seem to play an important role in the formation of BPE.

Abstract P3-02-08: Association of background parenchymal enhancement with breast cancer risk factors and tumor characteristics

Abstract Background: Background Parenchymal Enhancement (BPE) in a dynamic contrast enhanced (DCE)MRI refers to change in morphology and temporal degree of enhancement expressing physiology of breast tissue. Improvements in the analysis and interpretation of BPE have been shown to increase diagnostic accuracy of the MRI. We explored associations of BPE with breast cancer(BC) risk factors and tumor characteristics. Methods: We conducted a retrospective chart review study of 149 women who were treated for BC at the University of Maryland Medical Center(UMMC) between 2003-2015. Institutional Review Board approval was obtained. Subjects had BI-RADS 4,5 or 6 on their mammograms and/or biopsy proven BC. Women with false positive mammograms (n=2), missing BPE grade in their MRI reports (n=6), metastatic BC at diagnosis (n=2), MRIs done outside of a year of diagnosis (n=3), and who followed up outside UMMC (n=1) were excluded. Final analysis included 135 women. We used 1.5T or 3T scanners for DCE MRI. Standard contrast enhanced MRI protocol, T1, T2, and dynamic series were acquired according to American College of Radiology requirements for breast MRI accreditation and interpreted with Maximum Intensity Projection and subtracted imaging. Fellowship-trained breast imagers performed qualitative BPE assessment as per standard BI-RADS classification. BI-RADS classes were grouped as high BPE (BI-RADS moderate and marked) and low BPE (BI-RADS minimal and mild). Multivariable logistic regression was used to assess associations with predictors including individual and tumor characteristics. Results: Preliminary analyses showed BPE was significantly associated with alcohol intake, with drinkers being more likely to have high BPE compared to non-drinkers [odds ratio (OR) =3.08 (95% confidence interval (CI) = 1.34-7.09; p=0.008]. Women who received adjuvant radiation for their BC were less likely to have high BPE compared to women who did not undergo radiation [OR= 0.37 (95% CI=0.15-0.90); p=0.03] implying higher likelihood of breast conservation surgery in the former group. High BPE was less often observed for invasive ductal carcinoma histology vs. in-situ ductal carcinomas [OR= 0.40 95% CI=0.16-1.00); p=0.05] and in post- vs. premenopausal women [OR=0.47 (95%CI=0.22-1.00); p=0.05]. No association was found between BPE and race, BMI, prior HRT/OCP use, smoking, or ER/PR/HER-2 status. Conclusion: We observed association between higher BPE and alcohol exposure and lower BPE and, invasive ductal histology, postmenopausal status at diagnosis of BC and adjuvant radiation for BC. Larger studies are needed to corroborate our findings and identify the underlying mechanisms. Citation Format: Iyer L, Faramand R, Jung S, Rosenblatt P, Campassi C, Dromi S, Dorgan J, Tkaczuk K. Association of background parenchymal enhancement with breast cancer risk factors and tumor characteristics [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-02-08.

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

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

Quantitative assessment of background parenchymal enhancement in breast magnetic resonance images predicts the risk of breast cancer.

The objective of this study was to evaluate the association betweenthe quantitative assessment of background parenchymal enhancement rate (BPER) and breast cancer. From 14,033 consecutive patients who underwent breast MRI in our center, we randomly selected 101 normal controls. Then, we selected 101 women with benign breast lesions and 101 women with breast cancer who were matched for age and menstruation status. We evaluated BPER at early (2 minutes), medium (4 minutes) and late (6 minutes) enhanced time phases of breast MRI for quantitative assessment. Odds ratios (ORs) for risk of breast cancer were calculated using the receiver operating curve. The BPER increased in a time-dependent manner after enhancement in both premenopausal and postmenopausal women. Premenopausal women had higher BPER than postmenopausal women at early, medium and late enhanced phases. In the normal population, the OR for probability of breast cancer for premenopausal women with high BPER was 4.1 (95% CI: 1.7–9.7) and 4.6 (95% CI: 1.7–12.0) for postmenopausal women. The OR of breast cancer morbidity in premenopausal women with high BPER was 2.6 (95% CI: 1.1–6.4) and 2.8 (95% CI: 1.2–6.1) for postmenopausal women. The BPER was found to be a predictive factor of breast cancer morbidity. Different time phases should be used to assess BPER in premenopausal and postmenopausal women.

Background parenchymal enhancement over exam time in patients with and without breast cancer.

To compare background parenchymal enhancement (BPE) over time in patients with and without breast cancer. This retrospective Institutional Review Board (IRB)-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant study included 116 women (25-84 years, mean 54 years) with breast cancer who underwent breast magnetic resonance imaging at 3T between 1/2/2009 and 12/29/2009 and 116 age and date-of-exam-matched women without breast cancer (23-84 years, mean 51 years). Two independent, blinded readers (R1, R2) recorded BPE (minimal, mild, moderate, marked) at three times (100, 210, and 320 seconds postcontrast). Subsequent cancers were diagnosed in 9/96 control patients with follow up (12.6-93.0 months, mean 63.6 months). Exact Mann-Whitney, Fisher's exact, and McNemar tests were performed. Mean BPE was not found to be different between patients with and without breast cancer at any time (P = 0.36-0.64). At time 2 as compared with time 1, there were significantly more patients, both with and without breast cancer, with BPE >minimal (R1: 90 vs. 41 [P < 0.001] and 81 vs. 36 [P < 0.001]; R2: 84 vs. 52 [P < 0.001] and 79 vs. 43 [P < 0.001]) and BPE >mild (R1: 59 vs. 10 [P < 0.001] and 47 vs. 13 [P < 0.001]; R2: 49 vs. 12 [P < 0.001] and 41 vs. 18 [P < 0.001]). BPE changes between times 2 and 3 were not significant (P = 0.083-1.0). Odds ratios for control patients developing breast cancer were significant only for R2 and ranged up to 7.67 (1.49, 39.5; P < 0.01) for BPE >mild at time 2. BPE changes between the first and second postcontrast scans and stabilizes thereafter in most patients. Further investigation into the most clinically relevant timepoint for BPE assessment is warranted. 3 J. Magn. Reson. Imaging 2017;45:74-83.

Assessment of Background Parenchymal Enhancement and Lesion Kinetics in Breast MRI of BRCA 1/2 Mutation Carriers Compared to Matched Controls Using Quantitative Kinetic Analysis

To investigate whether quantitative kinetic analysis of lesions and background parenchyma in breast magnetic resonance imaging can elucidate differences between BRCA carriers and sporadic controls with high risk for breast cancer. Fifty-nine BRCA and 59 control cases (49 benign, 10 malignant) were examined in this study. Principal component analysis was applied for quantitative analysis of dynamic signal in background parenchyma (B) and lesion (L) in terms of initial enhancement ratio (IER) and delayed enhancement ratio (DER). Control B-IER, B-DER, L-IER, and L-DER were higher than BRCA cases in all women and in women with benign lesions; statistically significant differences in B-IER and B-DER (all women: P = 0.02 and P = 0.02, respectively; benign only: P = 0.005 and P = 0.005, respectively). In the control cohort, B-IER and B-DER were higher in the premenopausal women than in the postmenopausal women (P = 0.013 and 0.003, respectively), but not in the BRCA cohort; this led to significant differences in B-IER and B-DER between the control and the BRCA groups in the premenopausal women (P = 0.01 and 0.01, respectively) but not in the postmenopausal women. Results suggest possible differences in the vascular properties of background parenchyma between BRCA carriers and noncarriers and its association with menopausal status.