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

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.

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

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.

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.

Screening Breast MRI and the Science of Premenopausal Background Parenchymal Enhancement

The significance of background parenchymal enhancement (BPE) on screening and diagnostic breast MRI continues to be elucidated. Background parenchymal enhancement was initially deemed probably benign and followed or thought of as an artifact degrading the accuracy of breast cancer detection on breast MRI examinations. Subsequent research has focused on understanding the role of BPE regarding screening breast MRI. Today, there is growing evidence that a myriad of factors affect BPE, which in turn may influence patient outcomes. Additionally, BPE could represent an important risk factor for the future development of breast cancer. This article aims to describe the most up-to-date research on BPE as it relates to screening breast MRI in premenopausal women.

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.

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 classification of breast MRI background parenchymal enhancement using a transfer learning approach

Marked enhancement of the fibroglandular tissue on contrast-enhanced breast magnetic resonance imaging (MRI) may affect lesion detection and classification and is suggested to be associated with higher risk of developing breast cancer. The background parenchymal enhancement (BPE) is qualitatively classified according to the BI-RADS atlas into the categories "minimal," "mild," "moderate," and "marked." The purpose of this study was to train a deep convolutional neural network (dCNN) for standardized and automatic classification of BPE categories.This IRB-approved retrospective study included 11,769 single MR images from 149 patients. The MR images were derived from the subtraction between the first post-contrast volume and the native T1-weighted images. A hierarchic approach was implemented relying on 2 dCNN models for detection of MR-slices imaging breast tissue and for BPE classification, respectively. Data annotation was performed by 2 board-certified radiologists. The consensus of the 2 radiologists was chosen as reference for BPE classification. The clinical performances of the single readers and of the dCNN were statistically compared using the quadratic Cohen's kappa.Slices depicting the breast were classified with training, validation, and real-world (test) accuracies of 98%, 96%, and 97%, respectively. Over the 4 classes, the BPE classification was reached with mean accuracies of 74% for training, 75% for the validation, and 75% for the real word dataset. As compared to the reference, the inter-reader reliabilities for the radiologists were 0.780 (reader 1) and 0.679 (reader 2). On the other hand, the reliability for the dCNN model was 0.815.Automatic classification of BPE can be performed with high accuracy and support the standardization of tissue classification in MRI.

Association between background parenchymal enhancement and tumor response in patients with breast cancer receiving neoadjuvant chemotherapy

PURPOSE

To analyze the relationships between background parenchymal enhancement (BPE) of the contralateral healthy breast and tumor response after neoadjuvant chemotherapy (NAC) in women with breast cancer.

MATERIALS AND METHODS

A total of 228 women (mean age, 47.6 years±10 [SD]; range: 24-74 years) with invasive breast cancer who underwent NAC were included. All patients underwent breast magnetic resonance imaging (MRI) before and after NAC and 127 patients underwent MRI before, during (after the 4th cycle of NAC) and after NAC. Quantitative semi-automated analysis of BPE of the contralateral healthy breast was performed. Enhancement level on baseline MRI (baseline BPE) and MRI after chemotherapy (final BPE), change in enhancement rate between baseline MRI and final MRI (total BPE change) and between baseline MRI and midline MRI (early BPE change) were recorded. Associations between BPE and tumor response, menopausal status, tumor phenotype, NAC type and tumor stage at diagnosis were searched for. Pathologic complete response (pCR) was defined as the absence of residual invasive cancer cells in the breast and ipsilateral lymph nodes.

RESULTS

No differences were found in baseline BPE, final BPE, early and total BPE changes between pCR and non-pCR groups. Early BPE change was higher in non-pCR group in patients with stages 3 and 4 breast cancers (P=0.019) and in human epidermal growth factor receptor 2 (HER2)-negative patients (P=0.020).

CONCLUSION

Early reduction of BPE in the contralateral breast during NAC may be an early predictor of loss of tumor response, showing potential as an imaging biomarker of treatment response, especially in women with stages 3 or 4 breast cancers and in HER2 - negative breast cancers.

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).

Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net

RATIONALE AND OBJECTIVES

Breast segmentation using the U-net architecture was implemented and tested in independent validation datasets to quantify fibroglandular tissue volume in breast MRI.

MATERIALS AND METHODS

Two datasets were used. The training set was MRI of 286 patients with unilateral breast cancer. The segmentation was done on the contralateral normal breasts. The ground truth for the breast and fibroglandular tissue (FGT) was obtained by using a template-based segmentation method. The U-net deep learning algorithm was implemented to analyze the training set, and the final model was obtained using 10-fold cross-validation. The independent validation set was MRI of 28 normal volunteers acquired using four different MR scanners. Dice Similarity Coefficient (DSC), voxel-based accuracy, and Pearson's correlation were used to evaluate the performance.

RESULTS

For the 10-fold cross-validation in the initial training set of 286 patients, the DSC range was 0.83-0.98 (mean 0.95 ± 0.02) for breast and 0.73-0.97 (mean 0.91 ± 0.03) for FGT; and the accuracy range was 0.92-0.99 (mean 0.98 ± 0.01) for breast and 0.87-0.99 (mean 0.97 ± 0.01) for FGT. For the entire 224 testing breasts of the 28 normal volunteers in the validation datasets, the mean DSC was 0.86 ± 0.05 for breast, 0.83 ± 0.06 for FGT; and the mean accuracy was 0.94 ± 0.03 for breast and 0.93 ± 0.04 for FGT. The testing results for MRI acquired using four different scanners were comparable.

CONCLUSION

Deep learning based on the U-net algorithm can achieve accurate segmentation results for the breast and FGT on MRI. It may provide a reliable and efficient method to process large number of MR images for quantitative analysis of breast density.

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.