Dataset Information

Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

ABSTRACT:

Objectives

To evaluate sources of error in the Magnetic Resonance Imaging (MRI) measurement of percent fibroglandular tissue (%FGT) using two-point Dixon sequences for fat-water separation.

Methods

Ten female volunteers (median age: 31 yrs, range: 23-50 yrs) gave informed consent following Research Ethics Committee approval. Each volunteer was scanned twice following repositioning to enable an estimation of measurement repeatability from high-resolution gradient-echo (GRE) proton-density (PD)-weighted Dixon sequences. Differences in measures of %FGT attributable to resolution, T1 weighting and sequence type were assessed by comparison of this Dixon sequence with low-resolution GRE PD-weighted Dixon data, and against gradient-echo (GRE) or spin-echo (SE) based T1-weighted Dixon datasets, respectively.

Results

%FGT measurement from high-resolution PD-weighted Dixon sequences had a coefficient of repeatability of ±4.3%. There was no significant difference in %FGT between high-resolution and low-resolution PD-weighted data. Values of %FGT from GRE and SE T1-weighted data were strongly correlated with that derived from PD-weighted data (r = 0.995 and 0.96, respectively). However, both sequences exhibited higher mean %FGT by 2.9% (p < 0.0001) and 12.6% (p < 0.0001), respectively, in comparison with PD-weighted data; the increase in %FGT from the SE T1-weighted sequence was significantly larger at lower breast densities.

Conclusion

Although measurement of %FGT at low resolution is feasible, T1 weighting and sequence type impact on the accuracy of Dixon-based %FGT measurements; Dixon MRI protocols for %FGT measurement should be carefully considered, particularly for longitudinal or multi-centre studies.

SUBMITTER: Ledger AE

PROVIDER: S-EPMC4806997 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

Ledger Araminta E W AE Scurr Erica D ED Hughes Julie J Macdonald Alison A Wallace Toni T Thomas Karen K Wilson Robin R Leach Martin O MO Schmidt Maria A MA

PloS one 20160324 3

<h4>Objectives</h4>To evaluate sources of error in the Magnetic Resonance Imaging (MRI) measurement of percent fibroglandular tissue (%FGT) using two-point Dixon sequences for fat-water separation.<h4>Methods</h4>Ten female volunteers (median age: 31 yrs, range: 23-50 yrs) gave informed consent following Research Ethics Committee approval. Each volunteer was scanned twice following repositioning to enable an estimation of measurement repeatability from high-resolution gradient-echo (GRE) proton- ...[more]

PMID: 27011312

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Project description:Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment.In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's paired t-test, and Dice's similarity coefficients (DSC).The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers' manual segmentation, the proposed FCM-Atlas method achieves a correlation of r = 0.92 for FGT% and r = 0.93 for |FGT|, and the automated segmentation is not statistically significantly different (p = 0.46 for FGT% and p = 0.55 for |FGT|). The bilateral correlation between left breasts and right breasts for the FGT% is 0.94, 0.92, and 0.95 for reader 1, reader 2, and the FCM-Atlas, respectively; likewise, for the |FGT|, it is 0.92, 0.92, and 0.93, respectively. For the spatial segmentation agreement, the automated algorithm achieves a DSC of 0.69 ± 0.1 when compared to reader 1 and 0.61 ± 0.1 for reader 2, respectively, while the DSC between the two readers' manual segmentation is 0.67 ± 0.15. Additional robustness analysis shows that the segmentation performance of the authors' method is stable both with respect to selecting different cases and to varying the number of cases needed to construct the prior probability atlas. The authors' results also show that the proposed FCM-Atlas method outperforms the commonly used two-cluster FCM-alone method. The authors' method runs at ∼5 min for each 3D bilateral MR scan (56 slices) for computing the FGT% and |FGT|, compared to ∼55 min needed for manual segmentation for the same purpose.The authors' method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.

Project description:BackgroundResidual fibroglandular breast tissue (RFGT) following a mastectomy is associated with the remaining of occult breast cancer at the time of mastectomy as well as an increased local recurrence risk thereafter. Despite its oncologic implications, data on measures to prevent RFGT are lacking. Therefore, in a first step knowledge of risk factors for RFGT is of uttermost importance in order to allow identification of patients at risk and subsequently adaption of the surgical treatment and potentially prevention of RFGT a priori.MethodsWe performed a systematic literature review in PubMed using the MESH terms [residual fibroglandular breast tissue], [residual breast tissue], [mastectomy] and [risk factor] followed by a retrospective data analysis including all patients with a mastectomy treated at the Department of Obstetrics and Gynecology of the Medical University of Vienna, Austria, between 01.01.2015 and 26.02.2020 in order to identify risk factors of RFGT following a mastectomy. The primary aim of the study was to assess a potential difference in RFGT volume between the different types of mastectomy. The secondary objectives of the study were to identify other potential risk factors for RFGT as well as to compare the skin and subcutaneous fat tissue thickness pre- to postoperatively.ResultsSignificantly higher RFGT volumes were observed following a nipple-sparing mastectomy (NSM) compared to a skin-sparing mastectomy (SSM) and radical mastectomy (RME) (p < .001). Furthermore, RFGT volume was significantly associated with the variables: reconstruction (p = .012), acellular dermal matrix (ADM) or mesh (p = .031), patient age (p = .022), preoperative fibroglandular tissue (FGT) volume (p = .012) and preoperative whole breast volume (including the skin envelope and nipple-areola-complex) (p = .030). The reduction in the postoperative compared to preoperative skin envelope thickness measured medially and laterally reached statistical significance in the NSM-cohort (medial p < .001, lateral p = .001) and showed a numerical difference in the RME and SSM-cohort.ConclusionMastectomy type, reconstruction, ADM or mesh, patient age, preoperative FGT volume and whole breast volume were identified as risk factors for RFGT in univariable analysis. The observed reduction in the post- compared to preoperative skin envelope thickness should be avoided considering the known associated increase in risk for ischemic complications.

Project description:ObjectivesDevelopment of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification.MethodsFor the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed.ResultsTo assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006).ConclusionsGeneralizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required.Critical relevance statementA standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor.Key points• Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift.

Dataset Information

Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

Objectives

Methods

Results

Conclusion

Publications

Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue.

Similar Datasets

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