MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women

Brooks, Jennifer; Sung, Janice S.; Pike, Malcolm C.; Orlow, Irene; Stanczyk, Frank Z.; Bernstein, Jonine L.; Morris, Elizabeth A.

doi:10.1002/ijc.31370

Cited by 27 publications

(25 citation statements)

References 44 publications

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“…The lower enhancement classes occur more often than the higher ones, as can be seen from the review of 650 breast MRI examinations described by Abramovici and Mainiero [ 32 ] and in other studies. [ 30 , 32 ] This fact is reflected in the statistics of the radiologists’ answers, as reported in Table 3 . The readers classified the images to the lower classes more frequently, even though the BPE classes in the test dataset were equally represented.…”

Section: Discussionmentioning

confidence: 97%

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

Borkowski¹,

Rossi²,

Ciritsis³

et al. 2020

Medicine

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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.

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Section: Discussionmentioning

confidence: 97%

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

Borkowski¹,

Rossi²,

Ciritsis³

et al. 2020

Medicine

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“…In our study, amenorrhea was noted in 77% of the premenopausal women undergoing post-NAC MRI, but the degree of ovarian suppression probably differs between patients. BPE could thus give quantitative information about chemotherapy-induced ovarian suppression in premenopausal women 16 , as well as residual baseline estrogen levels in post-menopausal women 23 .…”

Section: Discussionmentioning

confidence: 99%

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

Molière

Oddou

Noblet

et al. 2019

Sci Rep

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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).

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“…Furthermore, in a study of 28 women who underwent breast MRIs before and after menopause, a significant number of women exhibited relatively lower BPE levels on postmenopausal MRIs compared with premenopausal MRIs . More recently, direct evidence of endogenous hormone levels and BPE has been uncovered, with a study demonstrating higher serum concentrations of estrone and estradiol present in postmenopausal women with elevated BPE levels …”

Section: Hormonal and Metabolic Influences On Bpementioning

confidence: 99%

Background parenchymal enhancement on breast MRI: A comprehensive review

Liao

Bancroft

Strigel

et al. 2019

Magnetic Resonance Imaging

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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 contrastenhanced 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

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MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women

Cited by 27 publications

References 44 publications

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

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

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

Background parenchymal enhancement on breast MRI: A comprehensive review

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