Breast Background Parenchymal Enhancement Research Articles

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 in breast 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.

Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status.

To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. Pre-menopausal women aged 40-50years old were scanned at 3T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

Abstract PD9-06: Additive benefit of MRI background parenchymal enhancement (BPE) to an MRI tumor volume model for predicting pathologic complete response (pCR) to neoadjuvant treatment in ISPY2

Abstract BACKGROUND: Background parenchymal enhancement (BPE) describes the natural phenomenon observed on breast MRI in which normal breast tissue demonstrates signal enhancement from uptake of intravenous contrast. BPE may provide independent and additive value for prediction of pathologic complete response (pCR) using MRI measured functional tumor volume (FTV), which has only moderate discrimination (FTV AUC ~ 0.7). We evaluated the additive value of quantitative whole breast BPE to a FTV model for prediction of pCR to neoadjuvant chemotherapy in the ISPY-2 trial. METHODS: In this HIPAA-compliant/IRB-approved study, women 18 years of age and older diagnosed with stage II or III breast cancer and with tumor size measured ≥ 2.5 cm were eligible to enroll in the I-SPY 2 TRIAL. Participants received a weekly dose of paclitaxel alone (control) or in combination with Veliparib and Carboplatin for 12 weekly cycles followed by four (every 2-3 weeks) cycles of anthracycline-cyclophosphamide prior to surgery. All breast cancers in these drug arms were Her2 negative. MRI was performed before the initiation of neoadjuvant therapy or “baseline” (T0), after three weeks of therapy or “early treatment” (T1), after twelve weeks between drug regimens or “inter-regimen” (T2), and after neoadjuvant therapy completion and prior to surgery or “pre-surgery” (T3). MRI segmentation was manually performed of the whole unaffected contralateral breast, and tissue classification was performed using fuzzy c-means clustering. BPE was calculated as the average enhancement of all tissue voxels at the first postcontrast acquisition. Predictor variables were parameterized as absolute values of BPE/FTV for each treatment time point or relative change values for each treatment time period. Logistic regression, stratified by hormone receptor (HR) subtype, was performed using 1) univariate models of FTV/BPE predictors alone and 2) multivariate models using all possible combinations of FTV/BPE predictors and HR status. Additive benefit for multivariate models was evaluated by estimating change in the area under the curve (AUC) for overall diagnostic performance with 10-repeat 5-fold cross validation. The 95% confidence interval (CI) of cross-validated AUC was estimated using 1,000 bootstrap resamples. RESULTS: A total of 88 patients (29 pCR, 59 non-pCR) were evaluated with serial breast MRIs to assess neoadjuvant response. Among univariate models, women with HR+ cancers who had PCR demonstrated a significantly greater decrease in BPE from baseline to pre-surgery compared to non-PCR (OR = 0.64, 95% CI = 0.39-0.92, p-value = 0.04). The associated AUC was 0.77 (95% CI 0.56-0.98), comparable to the range of univariate FTV AUC values (0.57-0.87). Among optimized multivariate models, the highest cross-validated AUC for FTV and HR predictors was 0.81 (95% CI 0.73-0.90), while adding BPE slightly increased AUC to 0.82 (95% CI 0.74-0.92). CONCLUSION: Changes in BPE in response to neoadjuvant therapy, which represents normal breast tissue changes measurable on any breast MRI, demonstrated significant association with pCR in women with HR+ breast cancer. Moreover, it had a similar diagnostic performance to univariate prediction with tumor volume. However, additive prediction of BPE to multivariate FTV models was only marginal. Citation Format: Vignesh A. Arasu, Paul Kim, Wen Li, Fredrik Strand, Cody McHargue, Ella Jones, David Newitt, John Kornak, Laura Esserman, Nola Hylton, ACRIN 6657 Trial TeamI-SPY Investigators Network. Additive benefit of MRI background parenchymal enhancement (BPE) to an MRI tumor volume model for predicting pathologic complete response (pCR) to neoadjuvant treatment in ISPY2 [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-06.

Quantitative background parenchymal enhancement to predict recurrence after neoadjuvant chemotherapy for breast cancer

Breast background parenchymal enhancement (BPE) is an increasingly studied MRI parameter that reflects the microvasculature of normal breast tissue, which has been shown to change during neoadjuvant chemotherapy (NAC) for breast cancer. We aimed at evaluating the BPE in patients undergoing NAC and its prognostic value to predict recurrence. MRI BPE was visually and quantitatively evaluated before and after NAC in a retrospective cohort of 102 women with unilateral biopsy-proven invasive breast cancer. Pre-therapeutic BPE was not predictive of pathological response or recurrence. Quantitative post-therapeutic BPE was significantly decreased compared to pre-therapeutic value. Post-therapeutic quantitative BPE significantly predicted recurrence (HR = 6.38 (0.71, 12.06), p < 0.05).

Is Breast Background Parenchymal Enhancement on MRI Related to BI-RADS Score and Follow-Up Rate?

Objective: We investigated the correlations between background parenchymal enhancement (BPE) and MRI interpretations with respect to short-interval follow-ups and biopsy rates. Methods: All accessible MRI examinations from 128 women during a limited time period in 2016 were evaluated. A blinded radiologist visually categorized BPE as minimal, mild, moderate, or marked. A BI-RADS category was also assigned. We used descriptive statistics to report the findings and chi-square and Fisher’s exact tests to compare categories. Results: Prevalence of minimal, mild, moderate, and marked BPE was 14.1%, 43.0%, 32.0%, and 10.9%, respectively. The short-interval follow-up rates were 22.2%, 27.3%, 26.8%, and 7.1% in women with minimal, mild, moderate, and marked BPE, respectively. BPE was not associated with the short-interval follow-up rate (p-value = 0.477). Biopsy rates were 22.2%, 27.3%, 22.0%, and 57.1% in women with minimal, mild, moderate, and marked BPE, respectively. Although there was no significant relationship between biopsy rates and BPE levels (p-value = 0.095) in the total population, these two factors were significantly associated in premenopausal women (p-value = 0.023) and in women of 30 - 39 years (p-value = 0.001). Conclusion: Higher BPE does not correlate with short-interval follow-up rates, but appears to be related to biopsy rate, thus causing false-positives and unnecessary biopsy recommendations, particularly in younger, premenopausal women.

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

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. 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. 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. 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.

Impact of radiotherapy on background parenchymal enhancement in breast magnetic resonance imaging.

While many studies have shown that hormones can influence background parenchymal enhancement (BPE) in breast magnetic resonance imaging (MRI), only few have directly address the effect of radiotherapy. The purpose of this study was to evaluate the impact of radiotherapy on BPE in breast MRI. A retrospective search identified 62 women with unilateral breast cancer who had a breast MRI both before and after radiotherapy following breast-conserving surgery. In our study, we assumed that systemic therapy affected both breasts equivalently. We rated the level of BPE of both breasts using a four- point categorical scale. A change in the level of BPE prior to and after treatment was compared in the diseased and contralateral breasts. All patients received a 4256 to 6480 cGy dose of whole breast radiotherapy over 3-7 weeks. The mean timing of the follow-up study was 6.6 months after completion of radiotherapy. Although the BPE showed a decrease in both breasts after treatment, there was a significant reduction of BPE in the irradiated breast compared with the contralateral breast (1.18 versus 0.98 average reduction in BPE level, p=0.042). Radiotherapy is associated with decrease in BPE with MRI.