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Wesselink E, Elliott J, Pool-Goudzwaard A, Coppieters M, Pevenage P, Di Ieva A, Weber II K. Quantifying lumbar paraspinal intramuscular fat: Accuracy and reliability of automated thresholding models. NORTH AMERICAN SPINE SOCIETY JOURNAL 2024; 17:100313. [PMID: 38370337 PMCID: PMC10869289 DOI: 10.1016/j.xnsj.2024.100313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/20/2024]
Abstract
Background The reported level of lumbar paraspinal intramuscular fat (IMF) in people with low back pain (LBP) varies considerably across studies using conventional T1- and T2-weighted magnetic resonance imaging (MRI) sequences. This may be due to the different thresholding models employed to quantify IMF. In this study we investigated the accuracy and reliability of established (two-component) and novel (three-component) thresholding models to measure lumbar paraspinal IMF from T2-weighted MRI. Methods In this cross-sectional study, we included MRI scans from 30 people with LBP (50% female; mean (SD) age: 46.3 (15.0) years). Gaussian mixture modelling (GMM) and K-means clustering were used to quantify IMF bilaterally from the lumbar multifidus, erector spinae, and psoas major using two and three-component thresholding approaches (GMM2C; K-means2C; GMM3C; and K-means3C). Dixon fat-water MRI was used as the reference for IMF. Accuracy was measured using Bland-Altman analyses, and reliability was measured using ICC3,1. The mean absolute error between thresholding models was compared using repeated-measures ANOVA and post-hoc paired sample t-tests (α = 0.05). Results We found poor reliability for K-means2C (ICC3,1 ≤ 0.38), moderate to good reliability for K-means3C (ICC3,1 ≥ 0.68), moderate reliability for GMM2C (ICC3,1 ≥ 0.63) and good reliability for GMM3C (ICC3,1 ≥ 0.77). The GMM (p < .001) and three-component models (p < .001) had smaller mean absolute errors than K-means and two-component models, respectively. None of the investigated models adequately quantified IMF for psoas major (ICC3,1 ≤ 0.01). Conclusions The performance of automated thresholding models is strongly dependent on the choice of algorithms, number of components, and muscle assessed. Compared to Dixon MRI, the GMM performed better than K-means and three-component performed better than two-component models for quantifying lumbar multifidus and erector spinae IMF. None of the investigated models accurately quantified IMF for psoas major. Future research is needed to investigate the performance of thresholding models in a more heterogeneous clinical dataset and across different sites and vendors.
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Affiliation(s)
- E.O. Wesselink
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences – Program Musculoskeletal Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - J.M. Elliott
- The University of Sydney, Faculty of Medicine and Health and the Northern Sydney Local Health District, The Kolling Institute, Sydney, Australia
| | - A. Pool-Goudzwaard
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences – Program Musculoskeletal Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- SOMT University of Physiotherapy, Amersfoort, The Netherlands
| | - M.W. Coppieters
- Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences – Program Musculoskeletal Health, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Menzies Health Institute Queensland, School of Health Sciences and Social Work, Griffith University, Brisbane and Gold Coast, Australia
| | | | - A. Di Ieva
- Computational Neurosurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Level 1, 75 Talavera Road, Sydney, NSW 2109, Australia
| | - K.A. Weber II
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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Lu LJW, Chen NW, Brunder DG, Nayeem F, Nagamani M, Nishino TK, Anderson KE, Khamapirad T. Soy isoflavones decrease fibroglandular breast tissue measured by magnetic resonance imaging in premenopausal women: A 2-year randomized double-blind placebo controlled clinical trial. Clin Nutr ESPEN 2022; 52:158-168. [PMID: 36513449 PMCID: PMC9825101 DOI: 10.1016/j.clnesp.2022.10.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/14/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND & AIMS Populations consuming soy have reduced risk for breast cancer, but the mechanisms are unclear. We tested the hypothesis that soy isoflavones, which have ovarian hormone-like effects, can reduce fibroglandular breast tissue (FGBT, 'breast density'), a strong risk marker for breast cancer. METHODS Premenopausal women (age 30-42 years) were randomized to consume isoflavones (136.6 mg as aglycone equivalents, n = 99) or placebo (n = 98) for 5 days per week up to 2 years, and changes in breast composition measured by magnetic resonance imaging at baseline and yearly intervals were compared after square root transformation using linear mixed effects regression models. RESULTS By intention-to-treat analyses (n = 194), regression coefficients (β estimates) of the interaction of time and isoflavone treatment were -0.238 (P = 0.06) and -0.258 (P < 0.05) before and after BMI adjustment, respectively for FGBT, 0.620 (P < 0.05) and 0.248 (P = 0.160), respectively for fatty breast tissue (FBT), and -0.155 (P < 0.05) and -0.107 (P < 0.05), respectively for FGBT as percent of total breast (FGBT%). β Estimates for interaction of treatment with serum calcium were -2.705 for FBT, and 0.588 for FGBT% (P < 0.05, before but not after BMI adjustment). BMI (not transformed) was related to the interaction of treatment with time (β = 0.298) or with calcium (β = -1.248) (P < 0.05). Urinary excretion of isoflavones in adherent subjects (n = 135) significantly predicted these changes in breast composition. Based on the modeling results, after an average of 1.2, 2.2 and 3.3 years of supplementation, a mean decrease of FGBT by 5.3, 12.1, and 19.3 cc, respectively, and a mean decrease of FGBT% by 1.37, 2.43, and 3.50%, respectively, were estimated for isoflavone exposure compared to placebo treatment. Subjects with maximum isoflavone excretion were estimated to have 38 cc less FGBT (or ∼3.13% less FGBT%) than subjects without isoflavone excretion. Decrease in FGBT and FGBT% was more precise with daidzein than genistein. CONCLUSIONS Soy isoflavones can induce a time- and concentration-dependent decrease in FGBT, a biomarker for breast cancer risk, in premenopausal women, and moderate effects of calcium on BMI and breast fat, suggesting a beneficial effect of soy consumption. TRIAL REGISTRATION www. CLINICALTRIALS gov identifier: NCT00204490. TRIAL REGISTRATION www. CLINICALTRIALS gov identifier: NCT00204490.
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Affiliation(s)
- Lee-Jane W Lu
- Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555-1109, USA.
| | - Nai-Wei Chen
- Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555-1109, USA.
| | - Donald G Brunder
- Academic Computing, The University of Texas Medical Branch, Galveston, TX 77555-1035, USA
| | - Fatima Nayeem
- Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555-1109, USA
| | - Manubai Nagamani
- Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Thomas K Nishino
- Radiology, The University of Texas Medical Branch, Galveston, TX 77555, USA.
| | - Karl E Anderson
- Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555-1109, USA.
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Henze Bancroft LC, Strigel RM, Macdonald EB, Longhurst C, Johnson J, Hernando D, Reeder SB. Proton density water fraction as a reproducible MR-based measurement of breast density. Magn Reson Med 2021; 87:1742-1757. [PMID: 34775638 DOI: 10.1002/mrm.29076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 01/12/2023]
Abstract
PURPOSE To introduce proton density water fraction (PDWF) as a confounder-corrected (CC) MR-based biomarker of mammographic breast density, a known risk factor for breast cancer. METHODS Chemical shift encoded (CSE) MR images were acquired using a low flip angle to provide proton density contrast from multiple echo times. Fat and water images, corrected for known biases, were produced by a six-echo CC CSE-MRI algorithm. Fibroglandular tissue (FGT) volume was calculated from whole-breast segmented PDWF maps at 1.5T and 3T. The method was evaluated in (1) a physical fat-water phantom and (2) normal volunteers. Results from two- and three-echo CSE-MRI methods were included for comparison. RESULTS Six-echo CC-CSE-MRI produced unbiased estimates of the total water volume in the phantom (mean bias 3.3%) and was reproducible across protocol changes (repeatability coefficient [RC] = 14.8 cm3 and 13.97 cm3 at 1.5T and 3.0T, respectively) and field strengths (RC = 51.7 cm3 ) in volunteers, while the two- and three-echo CSE-MRI approaches produced biased results in phantoms (mean bias 30.7% and 10.4%) that was less reproducible across field strengths in volunteers (RC = 82.3 cm3 and 126.3 cm3 ). Significant differences in measured FGT volume were found between the six-echo CC-CSE-MRI and the two- and three-echo CSE-MRI approaches (p = 0.002 and p = 0.001, respectively). CONCLUSION The use of six-echo CC-CSE-MRI to create unbiased PDWF maps that reproducibly quantify FGT in the breast is demonstrated. Further studies are needed to correlate this quantitative MR biomarker for breast density with mammography and overall risk for breast cancer.
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Affiliation(s)
| | - Roberta M Strigel
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Erin B Macdonald
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Clinical Imaging Physics Group, Duke University Medical Center, Durham, North Carolina, USA
| | - Colin Longhurst
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jacob Johnson
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Shamsabadi R, Baghani HR, Azadegan B, Mowlavi AA. Influence of breast tissue composition on dosimetric characteristics of therapeutic low energy X-rays. Radiat Phys Chem Oxf Engl 1993 2020. [DOI: 10.1016/j.radphyschem.2020.109110] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Quantitative Measurement of Breast Density Using Personalized 3D-Printed Breast Model for Magnetic Resonance Imaging. Diagnostics (Basel) 2020; 10:diagnostics10100793. [PMID: 33036272 PMCID: PMC7599838 DOI: 10.3390/diagnostics10100793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 11/17/2022] Open
Abstract
Despite the development and implementation of several MRI techniques for breast density assessments, there is no consensus on the optimal protocol in this regard. This study aimed to determine the most appropriate MRI protocols for the quantitative assessment of breast density using a personalized 3D-printed breast model. The breast model was developed using silicone and peanut oils to simulate the MRI related-characteristics of fibroglandular and adipose breast tissues, and then scanned on a 3T MRI system using non-fat-suppressed and fat-suppressed sequences. Breast volume, fibroglandular tissue volume, and percentage of breast density from these imaging sequences were objectively assessed using Analyze 14.0 software. Finally, the repeated-measures analysis of variance (ANOVA) was performed to examine the differences between the quantitative measurements of breast volume, fibroglandular tissue volume, and percentage of breast density with respect to the corresponding sequences. The volume of fibroglandular tissue and the percentage of breast density were significantly higher in the fat-suppressed sequences than in the non-fat-suppressed sequences (p < 0.05); however, the difference in breast volume was not statistically significant (p = 0.529). Further, a fat-suppressed T2-weighted with turbo inversion recovery magnitude (TIRM) imaging sequence was superior to the non-fat- and fat-suppressed T1- and T2-weighted sequences for the quantitative measurement of breast density due to its ability to represent the exact breast tissue compositions. This study shows that the fat-suppressed sequences tended to be more useful than the non-fat-suppressed sequences for the quantitative measurements of the volume of fibroglandular tissue and the percentage of breast density.
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Ma X, Wang J, Zheng X, Liu Z, Long W, Zhang Y, Wei J, Lu Y. Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks. Phys Med Biol 2020; 65:105006. [PMID: 32155611 DOI: 10.1088/1361-6560/ab7e7f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as a discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: (1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; (2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional (3D) bilateral breast MRI scans from 100 patients (aged 22-78 years) was used in this study with Institutional Review Board (IRB) approval. 3D hand-segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. The previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0 ± 7.0% and 77.6 ± 10.1%, respectively, while the corresponding values obtained through by the baseline method were 81.1 ± 8.7% and 69.0 ± 11.3%, respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between the quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46 ± 0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41 ± 0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.
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Affiliation(s)
- Xiangyuan Ma
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, People's Republic of China. Guangdong Province Key Laboratory Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China
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Sindi R, Sá Dos Reis C, Bennett C, Stevenson G, Sun Z. Quantitative Measurements of Breast Density Using Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. J Clin Med 2019; 8:jcm8050745. [PMID: 31137728 PMCID: PMC6571752 DOI: 10.3390/jcm8050745] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 05/22/2019] [Indexed: 02/06/2023] Open
Abstract
Breast density, a measure of dense fibroglandular tissue relative to non-dense fatty tissue, is confirmed as an independent risk factor of breast cancer. Although there has been an increasing interest in the quantitative assessment of breast density, no research has investigated the optimal technical approach of breast MRI in this aspect. Therefore, we performed a systematic review and meta-analysis to analyze the current studies on quantitative assessment of breast density using MRI and to determine the most appropriate technical/operational protocol. Databases (PubMed, EMBASE, ScienceDirect, and Web of Science) were searched systematically for eligible studies. Single arm meta-analysis was conducted to determine quantitative values of MRI in breast density assessments. Combined means with their 95% confidence interval (CI) were calculated using a fixed-effect model. In addition, subgroup meta-analyses were performed with stratification by breast density segmentation/measurement method. Furthermore, alternative groupings based on statistical similarities were identified via a cluster analysis employing study means and standard deviations in a Nearest Neighbor/Single Linkage. A total of 38 studies matched the inclusion criteria for this systematic review. Twenty-one of these studies were judged to be eligible for meta-analysis. The results indicated, generally, high levels of heterogeneity between study means within groups and high levels of heterogeneity between study variances within groups. The studies in two main clusters identified by the cluster analysis were also subjected to meta-analyses. The review confirmed high levels of heterogeneity within the breast density studies, considered to be due mainly to the applications of MR breast-imaging protocols and the use of breast density segmentation/measurement methods. Further research should be performed to determine the most appropriate protocol and method for quantifying breast density using MRI.
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Affiliation(s)
- Rooa Sindi
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
| | - Cláudia Sá Dos Reis
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Av. de Beaumont 21, 1011 Lausanne, Switzerland.
- CISP-Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, 1600-560 Lisboa, Portugal.
| | - Colleen Bennett
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
| | | | - Zhonghua Sun
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, Western Australia 6845, Australia.
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Pandey D, Yin X, Wang H, Su MY, Chen JH, Wu J, Zhang Y. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon 2018; 4:e01042. [PMID: 30582055 PMCID: PMC6299131 DOI: 10.1016/j.heliyon.2018.e01042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 11/04/2018] [Accepted: 12/10/2018] [Indexed: 12/13/2022] Open
Abstract
Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.
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Affiliation(s)
- Dinesh Pandey
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou 510006, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Min-Ying Su
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
| | - Jeon-Hor Chen
- Tu and Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
- Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Jianlin Wu
- Department of Radiology, Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
| | - Yanchun Zhang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
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Fibroglandular Tissue Quantification in Mammography by Optimized Fuzzy C-Means with Variable Compactness. Ing Rech Biomed 2017. [DOI: 10.1016/j.irbm.2017.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jiang L, Hu X, Xiao Q, Gu Y, Li Q. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images. Med Phys 2017; 44:2400-2414. [DOI: 10.1002/mp.12254] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 03/14/2017] [Accepted: 03/25/2017] [Indexed: 01/09/2023] Open
Affiliation(s)
- Luan Jiang
- Center for Advanced Medical Imaging Technology; Division of Life Sciences; Shanghai Advanced Research Institute; Chinese Academy of Sciences; No. 99 Haike Road Shanghai 201210 China
- Cooperate Research Center; Shanghai United Imaging Healthcare Co., Ltd.; No. 2258 Chengbei Road Shanghai 201807 China
| | - Xiaoxin Hu
- Department of Radiology; Shanghai Cancer Hospital of FuDan University; No. 270 DongAn Road Shanghai 200032 China
| | - Qin Xiao
- Department of Radiology; Shanghai Cancer Hospital of FuDan University; No. 270 DongAn Road Shanghai 200032 China
| | - Yajia Gu
- Department of Radiology; Shanghai Cancer Hospital of FuDan University; No. 270 DongAn Road Shanghai 200032 China
| | - Qiang Li
- Center for Advanced Medical Imaging Technology; Division of Life Sciences; Shanghai Advanced Research Institute; Chinese Academy of Sciences; No. 99 Haike Road Shanghai 201210 China
- Cooperate Research Center; Shanghai United Imaging Healthcare Co., Ltd.; No. 2258 Chengbei Road Shanghai 201807 China
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Pujara AC, Mikheev A, Rusinek H, Rallapalli H, Walczyk J, Gao Y, Chhor C, Pysarenko K, Babb JS, Melsaether AN. Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI. Clin Imaging 2017; 42:119-125. [DOI: 10.1016/j.clinimag.2016.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 11/07/2016] [Accepted: 12/02/2016] [Indexed: 10/20/2022]
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Ledger AEW, Scurr ED, Hughes J, Macdonald A, Wallace T, Thomas K, Wilson R, Leach MO, Schmidt MA. Comparison of Dixon Sequences for Estimation of Percent Breast Fibroglandular Tissue. PLoS One 2016; 11:e0152152. [PMID: 27011312 PMCID: PMC4806997 DOI: 10.1371/journal.pone.0152152] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 03/09/2016] [Indexed: 12/20/2022] Open
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.
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Affiliation(s)
- Araminta E. W. Ledger
- CR-UK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Erica D. Scurr
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Julie Hughes
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Alison Macdonald
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Toni Wallace
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Karen Thomas
- Clinical Research and Development, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Robin Wilson
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Martin O. Leach
- CR-UK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Maria A. Schmidt
- CR-UK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
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Chau A, Hua J, Taylor D. Analysing breast tissue composition with MRI using currently available short, simple sequences. Clin Radiol 2016; 71:287-92. [DOI: 10.1016/j.crad.2015.11.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 11/18/2015] [Accepted: 11/24/2015] [Indexed: 11/17/2022]
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Ng KH, Lau S. Vision 20/20: Mammographic breast density and its clinical applications. Med Phys 2015; 42:7059-77. [PMID: 26632060 DOI: 10.1118/1.4935141] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Kwan-Hoong Ng
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Susie Lau
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Calvo-Gallego JL, Martínez-Reina J, Domínguez J. A polynomial hyperelastic model for the mixture of fat and glandular tissue in female breast. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2015; 31:e02723. [PMID: 25950862 DOI: 10.1002/cnm.2723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Revised: 12/27/2014] [Accepted: 05/01/2015] [Indexed: 06/04/2023]
Abstract
In the breast of adult women, glandular and fat tissues are intermingled and cannot be clearly distinguished. This work studies if this mixture can be treated as a homogenized tissue. A mechanical model is proposed for the mixture of tissues as a function of the fat content. Different distributions of individual tissues and geometries have been tried to verify the validity of the mixture model. A multiscale modelling approach was applied in a finite element model of a representative volume element (RVE) of tissue, formed by randomly assigning fat or glandular elements to the mesh. Both types of tissues have been assumed as isotropic, quasi-incompressible hyperelastic materials, modelled with a polynomial strain energy function, like the homogenized model. The RVE was subjected to several load cases from which the constants of the polynomial function of the homogenized tissue were fitted in the least squares sense. The results confirm that the fat volume ratio is a key factor in determining the properties of the homogenized tissue, but the spatial distribution of fat is not so important. Finally, a simplified model of a breast was developed to check the validity of the homogenized model in a geometry similar to the actual one.
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Affiliation(s)
- Jose L Calvo-Gallego
- Department of Mechanical Engineering, School of Superior Engineering, University of Seville, Seville, Spain
| | - Javier Martínez-Reina
- Department of Mechanical Engineering, School of Superior Engineering, University of Seville, Seville, Spain
| | - Jaime Domínguez
- Department of Mechanical Engineering, School of Superior Engineering, University of Seville, Seville, Spain
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Sak MA, Littrup PJ, Duric N, Mullooly M, Sherman ME, Gierach GL. Current and Future Methods for Measuring Breast Density: A Brief Comparative Review. BREAST CANCER MANAGEMENT 2015; 4:209-221. [PMID: 28943893 PMCID: PMC5609705 DOI: 10.2217/bmt.15.13] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment.
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Affiliation(s)
- Mark A Sak
- Karmanos Cancer Institute, Wayne State University, 4100 John R Street, Detroit MI 48201
| | - Peter J Littrup
- Delphinus Medical Technologies, 46701 Commerce Center Dr, Plymouth, MI, 48170
- Brown University, Rhode Island Hospital, 593 Eddy Street, Providence RI, 02903
| | - Neb Duric
- Karmanos Cancer Institute, Wayne State University, 4100 John R Street, Detroit MI 48201
- Delphinus Medical Technologies, 46701 Commerce Center Dr, Plymouth, MI, 48170
| | - Maeve Mullooly
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Mark E Sherman
- Breast and Gynecologic Cancer Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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Abstract
OBJECTIVE. The purpose of this article is to review the use of MRI in breast density measurement and breast cancer risk estimation and to discuss the role of MRI as an alternative screening to mammography for screening women with dense breasts. CONCLUSION. The potential of MRI for screening women with dense breasts remains controversial because of the paucity of clinical evidence, the possibility of overdiagnosis, and the cost-effectiveness of the technique in this population. Although methods of MRI measurement require standardization and automation, future addition of MRI density to risk models may positively impact their value.
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Affiliation(s)
- Elizabeth A M O'Flynn
- 1 All authors: Institute of Cancer Research and Royal Marsden National Health Service Foundation Trust, Downs Rd, Sutton, Surrey SM2 5PT, United Kingdom
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18
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Wu S, Weinstein SP, Conant EF, Kontos D. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method. Med Phys 2014; 40:122302. [PMID: 24320533 DOI: 10.1118/1.4829496] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE 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. METHODS 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). RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Shandong Wu
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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Similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm. Int J Breast Cancer 2014; 2014:961679. [PMID: 25132995 PMCID: PMC4123610 DOI: 10.1155/2014/961679] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Revised: 06/02/2014] [Accepted: 06/03/2014] [Indexed: 01/16/2023] Open
Abstract
Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.
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Breast density assessment using a 3T MRI system: comparison among different sequences. PLoS One 2014; 9:e99027. [PMID: 24892933 PMCID: PMC4044003 DOI: 10.1371/journal.pone.0099027] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 05/09/2014] [Indexed: 11/23/2022] Open
Abstract
Purpose To compare MRI sequences for breast density measurements on a 3T MRI system using IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) as possible physiology-like reference. Materials and Methods MRI examination was performed in 48 consecutive patients (mean age 41, years; range, 35–67 years) on a 3.0T scanner and 46 were included. All (fertile) women, were examined between days 5 and 15 of their menstrual cycle. MRI protocol included: T1-turbo spin-echo (T1-tSE), T2-turbo spin-echo (T2-tSE), VIBRANT (Volume Imaging for Breast Assessment) before and after injection of contrast media and IDEAL. Breast density was calculated with semi-automated software. Statistical analysis was performed with non-parametric tests. Results Mean percentage of breast density calculated in each sequence was: T1-tSE = 56%; T2-tSE = 52%; IDEAL FatOnly = 55%; IDEAL WaterOnly = 53%, VIBRANT = 55%. Significant differences were observed between T2-tSE and both T1-tSE (p<0.001), VIBRANT sequences (p = 0.009), T1-tSE and both IDEAL WaterOnly (p = 0.007) and IDEAL FatOnly (p = 0.047). Breast density percentage showed a positive linear correlation among different sequences: r≥0.93. Conclusions Differences exist between MRI sequences used to assess breast density percentage. T1-weighted sequences values were similar to IDEAL sequences.
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