The correlation of background parenchymal enhancement in the contralateral breast with patient and tumor characteristics of MRI-screen detected breast cancers

Vreemann, Suzan; Gubern-Mérida, Albert; Borelli, Cristina; Bult, Peter; Karssemeijer, Nico; Mann, Ritse M.

doi:10.1371/journal.pone.0191399

Cited by 15 publications

(14 citation statements)

References 38 publications

(38 reference statements)

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“…Another limitation of the study is that our vessel detection method used T 1 ‐weighted, fat‐suppressed, and contrast‐enhanced subtraction MR images as inputs. Therefore, it is currently limited in patients with marked background parenchymal enhancement . Additionally, the individual AIFs were extracted from the thoracic arteries, which are small compared to the 3 × 3 windows used to generate the average signal intensity time course.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is currently limited in patients with marked background parenchymal enhancement. 44 Additionally, the individual AIFs were extracted from the thoracic arteries, which are small compared to the 3 × 3 windows used to generate the average signal intensity time course. This measurement could be affected by partial volume and motion artifacts, which could lead to an AIF with an underestimated enhancement rate and/or peak height.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Pineda

Hormuth

et al. 2018

Magnetic Resonance in Med

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Purpose We propose a novel methodology to integrate morphological and functional information of tumor‐associated vessels to assist in the diagnosis of suspicious breast lesions. Theory and Methods Ultrafast, fast, and high spatial resolution DCE‐MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE‐MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. Results A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans, and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). Conclusion This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Pineda

Hormuth

et al. 2018

Magnetic Resonance in Med

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“…In patients with invasive breast cancer, one study demonstrated an association between increased BPE and hormone (estrogen and progesterone) receptor positivity, the presence of lymphovascular invasion, and close surgical margins . Another study demonstrated that increased BPE was associated with lower tumor grade, as well progesterone receptor (PR) positivity . While these studies need to be further verified in larger, prospective studies, their findings point to the potential for BPE on preoperative breast MRI to provide prognostic information to help determine the need for neoadjuvant and/or adjuvant therapy and tailor optimal surgical approaches.…”

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

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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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“…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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The correlation of background parenchymal enhancement in the contralateral breast with patient and tumor characteristics of MRI-screen detected breast cancers

Cited by 15 publications

References 38 publications

Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Background parenchymal enhancement on breast MRI: A comprehensive review

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

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