Breast MRI phenotype and background parenchymal enhancement may predict tumor response to neoadjuvant endocrine therapy

Hilal, Talal; Covington, Matthew F.; Kosiorek, Heidi E.; Zwart, Christine M.; Ocal, Idris Tolgay; Pockaj, Barbara A.; Northfelt, Donald W.; Patel, Bhavika

doi:10.1111/tbj.13101

Cited by 24 publications

(25 citation statements)

References 12 publications

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“…Existing evidence suggests that BPE could be a predictor of neoadjuvant treatment response and varying BPE metrics may be associated with treatment outcomes (Tables and ). There are several retrospective cohort studies examining the association of BPE and changes in BPE with response to neoadjuvant chemotherapy (NAC) (Fig. ).…”

Section: Bpe As a Predictor Of Treatment Outcomesmentioning

confidence: 99%

“…A few studies examined the relationship between baseline (pre‐NAC) BPE and varying metrics of tumor response and found no significant association . One study examined the association between baseline BPE and response to neoadjuvant endocrine therapy for estrogen receptor (ER)‐positive and HER‐2‐negative tumors and found an inverse association—lower BPE (minimal or mild) was associated with better response, defined as stable or reduced tumor size on clinical or ultrasound examination …”

Section: Bpe As a Predictor Of Treatment Outcomesmentioning

confidence: 99%

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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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Section: Bpe As a Predictor Of Treatment Outcomesmentioning

confidence: 99%

Section: Bpe As a Predictor Of Treatment Outcomesmentioning

confidence: 99%

Background parenchymal enhancement on breast MRI: A comprehensive review

Liao

Bancroft

Strigel

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…No previous research has investigated the performance of clinical and MRI assessment methods in the evaluation of residual tumour size in patients receiving combined neoadjuvant endocrine therapy with third generation aromatase inhibitors [21,22]. Aromatase inhibitors (AIs) are well-established and low-toxicity drugs to improve surgical outcomes in LABC in elderly or otherwise fragile and postmenopausal patients, particularly in selected patients with oestrogen receptor (ER) highly (> 50%) positive disease [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Reis

Lindstrøm

Boavida

et al. 2020

Breast Cancer Res Treat

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Purpose To assess the accuracy of magnetic resonance imaging (MRI) measurements in locally advanced oestrogen receptor-positive and human epidermal growth factor receptor 2-negative breast tumours before, during and after neoadjuvant endocrine treatment (NET) for evaluation of tumour response in comparison with clinical and pathological assessments. Methods This prospective study enrolled postmenopausal patients treated neoadjuvant with letrozole and exemestane given sequentially in an intra-patient cross-over regimen. Fifty-four patients were initially recruited, but only 35 fulfilled the inclusion criteria and confirmed to participate with a median age of 77. Tumours were scanned with MRI prior to treatment, during the eighth week of treatment and prior to surgery. Additionally, changes in longest diameter on clinical examination (CE) and tumour size at pathology were determined. Pre- and post-operative measurements of tumour size were compared in order to evaluate tumour response. Results The correlation between post-treatment MRI size and pathology was moderate and higher with a correlation coefficient (r) 0.64 compared to the correlation between CE and pathology r = 0.25. Post-treatment MRI and clinical results had a negligible bias towards underestimation of lesion size. Tumour size on MRI and CE had 0.82 cm and 0.52 cm lower mean size than tumour size measured by pathology, respectively. Conclusions The higher correlation between measurements of residual disease obtained on MRI and those obtained with pathology validates the accuracy of imaging assessment during NET. MRI was found to be more accurate for estimating complete responses than clinical assessments and warrants further investigation in larger cohorts to validate this finding.

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“…Previous studies have shown that the relative amounts of FGT on mammography are associated with the risk of breast cancer 2–4 . Also, recent studies have proved that increased BPE is associated with increased risk of breast cancer 5–10 and overall treatment outcomes 11–15 …”

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confidence: 99%

Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine‐Learning Models

Nam

Park

Kang

et al. 2020

Magnetic Resonance Imaging

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Background Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). Purpose To develop and evaluate a machine‐learning algorithm for breast FGT segmentation and BPE classification. Study Type Retrospective. Population A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set. Field Strength/Sequence 3T and 1.5T; T2‐weighted, fat‐saturated T1‐weighted (T1W) with dynamic contrast enhancement (DCE). Assessment Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre‐ and postcontrast T1W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep‐learning‐based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. Statistical Tests Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep‐learning‐based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification. Results The mean (±SD) DSC for manual and deep‐learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual‐ and deep‐learning‐based segmentations was 0.93. Overall accuracy of manual segmentation and deep‐learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep‐learning segmentation; the AUC was 0.93 in both methods. Data Conclusion This deep‐learning‐based algorithm can provide reliable segmentation and classification results for BPE. Level of Evidence 3 Technical Efficacy Stage 2

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Breast MRI phenotype and background parenchymal enhancement may predict tumor response to neoadjuvant endocrine therapy

Cited by 24 publications

References 12 publications

Background parenchymal enhancement on breast MRI: A comprehensive review

Background parenchymal enhancement on breast MRI: A comprehensive review

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine‐Learning Models

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