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

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

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.

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.

[Impact of the menstrual cycle on the quality of interpretation of the MRI result in the follow-up of women at genetic risk for breast]

INTRODUCTION

Breast MRI is used as a reference for screening breast cancer among women with a genetic high risk. Its sensitivity and specificity might decrease because of the background parenchymal enhancement. Therefore, it is recommended to plan the MRI between the 7th and the 14th day of the menstrual cycle despite of the burden of this organization. Our aim was to evaluate the interpretation (performance) of the MRI performance when it was done out of this period.

METHODS

We analyzed the MRI done in the Tenon Hospital among patients with a genetic high risk, without a history of breast cancer, between 2006 and 2016. We analyzed the rate of enhancement hindering the interpretation (EH) - that is to say grade III and IV -, the rate of additional explorations (MRI and biopsy), and the occurrence of interval events in 2 groups according to the programming of the examination: appropriate programming (D7-D14) and inappropriate programming (outside this period).

RESULTS

In total, 126 MRI were analyzed, done in a population of 62 women with a genetic predisposition to Breast Cancer (BRCA 1 or 2: 91%, others: 9%), median age was 34.5 years old. 84 were in appropriate programming and 42 were in an inappropriate one. The rate of EH was comparable between the two groups (respectively 31% and 35.7%, P=1), as well as the rate of additional explorations (respectively 31% and 45%, P=0.11).

CONCLUSION

Our results suggest that the programming of screening MRI could be simplified among patients with a genetic predisposition of breast cancer.

The rate of breast fibroglandular enhancement during dynamic contrast-enhanced MRI reflects response to neoadjuvant therapy

PURPOSE

This study assesses the rate of enhancement of breast fibroglandular tissue after administration of a magnetic resonance imaging (MRI) gadolinium-based contrast agent and determines its relationship with response to neoadjuvant therapy (NAT) in women with breast cancer.

METHOD

Women with locally advanced breast cancer (N = 19) were imaged four times over the course of NAT. Dynamic contrast-enhanced (DCE) MRI was acquired after administration of a gadolinium-based contrast agent with a temporal resolution of 7.27 s. The tumor, fibroglandular tissue, and adipose tissue were semi-automatically segmented using a manually drawn region of interest encompassing the tumor followed by fuzzy c-means clustering. The rate and relative intensity of signal enhancement were calculated for each voxel within the tumor and fibroglandular tissue.

RESULTS

The rate of fibroglandular tissue enhancement after contrast agent injection declined by an average of 29 % over the course of NAT. This decline was present in 16 of the 19 patients in the study. The rate of enhancement is significantly higher in women who achieve pathological complete response (pCR) after both 1 cycle (68 % higher, p < 0.05) and after 3-5 cycles of NAT (58 % higher; p < 0.05). The relative intensity of fibroglandular enhancement correlates with the rate of enhancement (R2 = 0.64, p < 0.001) and is higher in women who achieve pCR after both 1 cycle and after 3-5 cycles of NAT (p < 0.05, both timepoints).

CONCLUSION

The rate of fibroglandular tissue enhancement declines over the course of therapy, provides novel information not reflected by tumoral measures, and may predict pathological response early in the course of therapy, with smaller declines in enhancement in women who achieve favorable response.

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.

Correlations of female hormone levels with background parenchymal enhancement and apparent diffusion coefficient values in premenopausal breast cancer patients: Effects on cancer visibility

PURPOSE

To evaluate the relationships between female hormone levels and background parenchymal enhancement (BPE) or apparent diffusion coefficient (ADC) values of breast parenchyma, as well as the effects of BPE and ADC values on cancer visibility.

METHODS

This prospective study was performed in 164 consecutive premenopausal patients who were diagnosed with invasive breast cancer from November 2016 to December 2018. Two radiologists analyzed the qualitative, quantitative BPE and ADC values of normal contralateral breast parenchyma. We also analyzed the cancer visibility using a three-point scale (0: no visibility, 1: slight visibility, 2: excellent visibility).

RESULTS

The progesterone level was significantly correlated with qualitative BPE grade and quantitative values of the BPE, as well as with the mean ADC. On contrast enhanced image (CEI), the visibility score was significantly associated with tumor size, qualitative and quantitative BPE. On diffusion weighted image (DWI), tumor size was significantly associated with the visibility score, whereas the ADC value was not. Of four lesions with a score of 0 on CEI, three had a score of 2 and one a score of 1 on DWI. Regarding the visibility score on DWI, tumor size and histologic type were significantly different among the three groups.

CONCLUSIONS

Qualitative or quantitative BPE of breast parenchyma was positively correlated with the progesterone level and the mean ADC was negatively correlated. The cancer visibility was affected by BPE on CEI, but not by ADC on DWI. Small-sized cancer and invasive lobular cancer could be the causes of false-negative diagnoses on DWI.

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.

Changes in total choline concentration in the breast of healthy fertile young women in relation to menstrual cycle or use of oral contraceptives: a 3-T 1H-MRS study

Background

To evaluate changes in total choline (tCho) absolute concentration ([tCho]) in the breast of healthy fertile women in relation to menstrual cycle (MC) or use of oral contraceptives (OC).

Methods

After institutional review board approval, we prospectively evaluated 40 healthy fertile volunteers: 20 with physiological MC, aged 28 ± 3 years (mean ± standard deviation; nOC group); 20 using OC, aged 26 ± 3 years (OC group). Hormonal assays and water-suppressed single-voxel 3-T proton magnetic resonance spectroscopy (¹H-MRS) were performed on MC days 7, 14, and 21 in the nOC group and only on MC day 14 in the OC group. [tCho] was measured versus an external phantom. Mann-Whitney U test and Spearman coefficient were used; data are given as median and interquartile interval.

Results

All spectra had good quality. In the nOC group, [tCho] (mM) did not change significantly during MC: 0.8 (0.3–2.4) on day 7, 0.9 (0.4–1.2) on day 14, and 0.4 (0.2–0.8) on day 21 (p = 0.963). In the OC group, [tCho] was 0.7 (0.2–1.7) mM. The between-groups difference was not significant on all days (p ≥ 0.411). All hormones except prolactin changed during MC (p ≤ 0.024). In the OC group, [tCho] showed a borderline correlation with estradiol (r = 0.458, p = 0.056), but no correlation with other hormones (p ≥ 0.128). In the nOC group, [tCho] negatively correlated with prolactin (r = -0.587, p = 0.006) on day 7; positive correlation was found with estradiol on day 14 (r = 0.679, p = 0.001).

Conclusions

A tCho peak can be detected in the normal mammary gland using 3-T ¹H-MRS. The [tCho] in healthy volunteers was 0.4–0.9 mM, constant over the MC and independent of OC use.