MRI Texture Analysis of Background Parenchymal Enhancement of the Breast

Amano, Yasuo; Woo, Jun; Amano, Maki; Yanagisawa, Fumi; Yamamoto, Hiroshi; Tani, Mayumi

doi:10.1155/2017/4845909

Cited by 18 publications

(16 citation statements)

References 21 publications

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“…When BPE is reported as the volume or percentage of overall fibroglandular tissue, a threshold is typically applied to the voxelwise calculation of PE to define the voxels considered “enhanced.” The definition of this threshold varies from study to study, but choices have included thresholds selected based on the noise characteristics of the images, a fraction of the maximum enhancement seen in an enhancing lesion, or thresholds based on analysis of the enhancement patterns seen in the images assigned to different qualitative BPE categories . While a quantitative value derived in some manner from the calculation of PE is most commonly described, other values have also been calculated, including SER, model‐based fitting parameters, and features related to texture analysis …”

Section: Approaches For Bpe Quantitationmentioning

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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Section: Approaches For Bpe Quantitationmentioning

confidence: 99%

Background parenchymal enhancement on breast MRI: A comprehensive review

Liao

Bancroft

Strigel

et al. 2019

Magnetic Resonance Imaging

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“…Indeed, the evaluation of a large quantity of 4D-DCE images for each patient is a time-consuming process, and their interpretation requires experienced radiologists [ 14 ]. In literature, textural analysis techniques have been applied to dynamic breast MRI to quantify BPE [ 15 ], discriminate malignant from benign tissue [ 16 – 18 ], or identify particular topology of lesions [ 19 ]. Therefore, the focus of the main works at the state of the art regards diagnostic studies of diseases.…”

Section: Introductionmentioning

confidence: 99%

A Gradient-Based Approach for Breast DCE-MRI Analysis

Losurdo

Basile

Fanizzi

et al. 2018

BioMed Research International

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Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

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“…Since some investigators believed that when BPE manifests as asymmetric, regional, or focal distribution, it was di cult to distinguish BPE from NME [25][26][27]. Moreover, BPE might interfere with the delineation of tumor boundaries [28]. However, this major factor that affected the diagnostic accuracy of NME in premenopausal women was ignored by previous studies; Second, the interpretation of morphologic features in MR images was highly dependent on the radiologist's experience level and lacked reproducibility.…”

Section: Discussionmentioning

confidence: 99%

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

Tan

Mai

Huang

et al. 2020

Preprint

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Background: Non-mass enhancement (NME) is a diagnostic dilemma. Texture analysis (TA) could serve as an objective method to quantify tumor characteristics and growth patterns. However, there are few reports about TA use in NME diagnosis. To our knowledge, NME diagnosis based on the combination of the features noted on routine MRI and TA has not been reported.. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. Methods: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson’s chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis.Results: The combination model showed a superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising.Conclusion: The combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

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MRI Texture Analysis of Background Parenchymal Enhancement of the Breast

Cited by 18 publications

References 21 publications

Background parenchymal enhancement on breast MRI: A comprehensive review

Background parenchymal enhancement on breast MRI: A comprehensive review

A Gradient-Based Approach for Breast DCE-MRI Analysis

Additive Value of Texture Analysis Based on Breast MRI for Distinguishing Between Benign and Malignant Non-mass Enhancement in Premenopausal Women

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