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Eom HJ, Cha JH, Choi WJ, Cho SM, Jin K, Kim HH. Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis. Acta Radiol 2024; 65:708-715. [PMID: 38825883 DOI: 10.1177/02841851241257794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
BACKGROUND Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. PURPOSE To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. MATERIAL AND METHODS A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. RESULTS Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. CONCLUSION The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).
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Affiliation(s)
- Hye Joung Eom
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joo Hee Cha
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woo Jung Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Su Min Cho
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kiok Jin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hak Hee Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Lew CO, Harouni M, Kirksey ER, Kang EJ, Dong H, Gu H, Grimm LJ, Walsh R, Lowell DA, Mazurowski MA. A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI. Sci Rep 2024; 14:5383. [PMID: 38443410 PMCID: PMC10915139 DOI: 10.1038/s41598-024-54048-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/08/2024] [Indexed: 03/07/2024] Open
Abstract
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model's predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available.
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Affiliation(s)
- Christopher O Lew
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA.
| | - Majid Harouni
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Ella R Kirksey
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Elianne J Kang
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Haoyu Dong
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Hanxue Gu
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Ruth Walsh
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Dorothy A Lowell
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
| | - Maciej A Mazurowski
- Department of Radiology, Duke University Medical Center, Box 2731, Durham, NC, 27710, USA
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3
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Tagliafico AS, Houssami N. Towards consensus on managing high mammographic density in population breast screening? Breast 2023; 69:422-423. [PMID: 37167927 DOI: 10.1016/j.breast.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/13/2023] Open
Affiliation(s)
- Alberto Stefano Tagliafico
- Radiology Section, Department of Health Sciences (DISSAL), University of Genova, Via Pastore 1, 16132, Genoa, Italy; Department of Radiology, IRCCS-Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy.
| | - Nehmat Houssami
- The Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council NSW, Sydney, New South Wales, Australia; Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
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4
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Singh N, Joshi P, Singh DK, Narayan S, Gupta A. Volumetric breast density evaluation using fully automated Volpara software, its comparison with BIRADS density types and correlation with the risk of malignancy. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00796-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Mammography is currently the modality of choice for mass screening of breast cancer, although its sensitivity is low in dense breasts. Besides, higher breast density has been identified as independent risk factor so it has been conceptualized that women with dense breasts should be encouraged for supplemental screening. In this study, we aimed to estimate the distribution of volumetric breast density using fully automated Volpara software and to analyze the level of agreement between volumetric density grades and Breast Imaging Reporting and Data System (BI-RADS) density grades. We also aim to estimate the distribution of breast cancer in different VDG and to find a correlation between VDG and risk of malignancy.
Results
VDG-c was most common followed by VDG-b and BIRADS grade B was commonest followed by grade C. The density distribution was found inversely related to the age. Level of agreement between VDG and BIRADS grades was moderate (κ = 0.5890). Statistically significant correlation was noted between VDG-c and d for risk of malignancy (p < 0.001).
Conclusion
Difficulties associated with the use of BI-RADS density categories may be avoided if assessed using a fully automated volumetric method. High VDG can be considered as independent risk factor for malignancy. Thus, awareness of a woman’s breast density might be useful in determining the frequency and imaging modality for screening.
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Aloufi AS, AlNaeem AN, Almousa AS, Hashem AM, Malik MA, Altahan FM, Elsharkawi MM, Almasar KA, ElMahdy MH, Squires SE, Alzimami KS, Harkness EF, Astley SM. Mammographic breast density and breast cancer risk in the Saudi population: a case-control study using visual and automated methods. Br J Radiol 2022; 95:20211197. [PMID: 35195439 PMCID: PMC10996407 DOI: 10.1259/bjr.20211197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/04/2022] [Accepted: 02/14/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods. METHODS 1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density™ and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest vs lowest quartiles in VAS, pVAS and Volpara Density™, were estimated using conditional logistic regression. RESULTS For BI-RADS, the OR was 6.69 (95% CI 2.79-16.06) in the highest vs second category and OR = 4.78 (95% CI 3.01-7.58) in the highest vs lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86-14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68-10.77) and Volpara Density ™ OR = 3.55, (95% CI 1.86-6.75) for highest vs lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65-0.75) demonstrating better discrimination between cases and controls than all other methods. CONCLUSION Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists' visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density. ADVANCES IN KNOWLEDGE Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.
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Affiliation(s)
- Areej S Aloufi
- Division of Informatics, Imaging and Data Sciences, Faculty of
Biology, Medicine and Health, University of Manchester,
Manchester, UK
- Department of Radiological Sciences, College of Applied Medical
Sciences, King Saud University,
Riyadh, Kingdom of Saudi
Arabia
| | - Abdulrahman N AlNaeem
- Department of Women’s Imaging, King Fahad Medical
City, Riyadh, Kingdom of
Saudi Arabia
| | - Abeer S Almousa
- Department of Women’s Imaging, King Fahad Medical
City, Riyadh, Kingdom of
Saudi Arabia
| | - Amani M Hashem
- Department of Breast Imaging, King Khaled University
Hospital, Riyadh, Kingdom
of Saudi Arabia
| | - Mehreen A Malik
- Department of Breast Imaging, King Khaled University
Hospital, Riyadh, Kingdom
of Saudi Arabia
| | | | | | | | - Manal H ElMahdy
- Department of Radiological Sciences, College of Applied Medical
Sciences, King Saud University,
Riyadh, Kingdom of Saudi
Arabia
| | - Steven E Squires
- Division of Informatics, Imaging and Data Sciences, Faculty of
Biology, Medicine and Health, University of Manchester,
Manchester, UK
| | - Khalid S Alzimami
- Department of Radiological Sciences, College of Applied Medical
Sciences, King Saud University,
Riyadh, Kingdom of Saudi
Arabia
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, Faculty of
Biology, Medicine and Health, University of Manchester,
Manchester, UK
- Nightingale Breast Screening Centre & Prevent Breast Cancer
Unit, Wythenshawe Hospital, Manchester University NHS Foundation
Trust,, Manchester M23 9LT,
UK
| | - Susan M Astley
- Division of Informatics, Imaging and Data Sciences, Faculty of
Biology, Medicine and Health, University of Manchester,
Manchester, UK
- Nightingale Breast Screening Centre & Prevent Breast Cancer
Unit, Wythenshawe Hospital, Manchester University NHS Foundation
Trust,, Manchester M23 9LT,
UK
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Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography. Insights Imaging 2021; 12:147. [PMID: 34674061 PMCID: PMC8531174 DOI: 10.1186/s13244-021-01093-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/09/2021] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To assess the similarity and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. METHODS 165 consecutive women who underwent FFDM were included. Breasts have been segmented into "dense" and "non-dense" area using the software LIBRA. Segmentation of both FOR PROCESSING and FOR PRESENTATION images have been evaluated by Bland-Altman, Dice index and Cohen's kappa analysis. 74 textural features were computed: 18 features of First Order (FO), 24 features of Gray Level Co-occurrence Matrix (GLCM), 16 features of Gray Level Run Length Matrix (GLRLM) and 16 features of Gray Level Size Zone Matrix (GLSZM). Paired Wilcoxon test, Spearman's rank correlation, intraclass correlation and canonical correlation have been used. Bilateral symmetry and percent density (PD) were also evaluated. RESULTS Segmentation from FOR PROCESSING and FOR PRESENTATION gave very different results. Bilateral symmetry was higher when evaluated on features computed using FOR PROCESSING images. All features showed a positive Spearman's correlation coefficient and many FOR-PROCESSING features were moderately or strongly correlated to their corresponding FOR-PRESENTATION counterpart. As regards the correlation analysis between PD and textural features from FOR-PRESENTATION a moderate correlation was obtained only for Gray Level Non Uniformity from GLRLM both on "dense" and "non dense" area; as regards correlation between PD and features from FOR-PROCESSING a moderate correlation was observed only for Maximal Correlation Coefficient from GLCM both on "dense" and "non dense" area. CONCLUSIONS Texture features from FOR PROCESSING mammograms seem to be most suitable for assessing breast density.
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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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8
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Sindi R, Wong YH, Yeong CH, Sun Z. Development of patient-specific 3D-printed breast phantom using silicone and peanut oils for magnetic resonance imaging. Quant Imaging Med Surg 2020; 10:1237-1248. [PMID: 32550133 DOI: 10.21037/qims-20-251] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues. Methods A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells. Results The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times. Conclusions A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.
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Affiliation(s)
- Rooa Sindi
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, WA, Australia.,Radio-diagnostic and Medical Imaging Department, Medical Physics Section, King Fahd Armed Forces Hospital, Jeddah, Kingdom of Saudi Arabia
| | - Yin How Wong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Selangor, Malaysia
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Selangor, Malaysia
| | - Zhonghua Sun
- Discipline of Medical Radiation Sciences, School of Molecular and Life Sciences, Curtin University, Perth, WA, Australia
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Alomaim W, O’Leary D, Ryan J, Rainford L, Evanoff M, Foley S. Subjective Versus Quantitative Methods of Assessing Breast Density. Diagnostics (Basel) 2020; 10:diagnostics10050331. [PMID: 32455552 PMCID: PMC7277954 DOI: 10.3390/diagnostics10050331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 11/16/2022] Open
Abstract
In order to find a consistent, simple and time-efficient method of assessing mammographic breast density (MBD), different methods of assessing density comparing subjective, quantitative, semi-subjective and semi-quantitative methods were investigated. Subjective MBD of anonymized mammographic cases (n = 250) from a national breast-screening programme was rated by 49 radiologists from two countries (UK and USA) who were voluntarily recruited. Quantitatively, three measurement methods, namely VOLPARA, Hand Delineation (HD) and ImageJ (IJ) were used to calculate breast density using the same set of cases, however, for VOLPARA only mammographic cases (n = 122) with full raw digital data were included. The agreement level between methods was analysed using weighted kappa test. Agreement between UK and USA radiologists and VOLPARA varied from moderate (κw = 0.589) to substantial (κw = 0.639), respectively. The levels of agreement between USA, UK radiologists, VOLPARA with IJ were substantial (κw = 0.752, 0.768, 0.603), and with HD the levels of agreement varied from moderate to substantial (κw = 0.632, 0.680, 0.597), respectively. This study found that there is variability between subjective and objective MBD assessment methods, internationally. These results will add to the evidence base, emphasising the need for consistent, simple and time-efficient MBD assessment methods. Additionally, the quickest method to assess density is the subjective assessment, followed by VOLPARA, which is compatible with a busy clinical setting. Moreover, the use of a more limited two-scale system improves agreement levels and could help minimise any potential country bias.
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Affiliation(s)
- Wijdan Alomaim
- Radiography & Medical Imaging, Fatima College of Health Sciences, Abu Dhabi, UAE
- Correspondence: ; Tel.: +9712-5078639
| | - Desiree O’Leary
- Radiography (Diagnostic Imaging), Keele University, Keele ST5 5BG, UK; D.s.o'
| | - John Ryan
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
| | - Louise Rainford
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
| | | | - Shane Foley
- Radiography & Diagnostic Imaging, School of Medicine, University College Dublin, 4 Dublin, Ireland; (J.R.); (L.R.); (S.F.)
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10
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Update on Breast Density, Risk Estimation, and Supplemental Screening. AJR Am J Roentgenol 2020; 214:296-305. [DOI: 10.2214/ajr.19.21994] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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11
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Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI. J Digit Imaging 2019; 31:425-434. [PMID: 29047034 DOI: 10.1007/s10278-017-0031-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Mammographic breast density (MBD) is the most commonly used method to assess the volume of fibroglandular tissue (FGT). However, MRI could provide a clinically feasible and more accurate alternative. There were three aims in this study: (1) to evaluate a clinically feasible method to quantify FGT with MRI, (2) to assess the inter-rater agreement of MRI-based volumetric measurements and (3) to compare them to measurements acquired using digital mammography and 3D tomosynthesis. This retrospective study examined 72 women (mean age 52.4 ± 12.3 years) with 105 disease-free breasts undergoing diagnostic 3.0-T breast MRI and either digital mammography or tomosynthesis. Two observers analyzed MRI images for breast and FGT volumes and FGT-% from T1-weighted images (0.7-, 2.0-, and 4.0-mm-thick slices) using K-means clustering, data from histogram, and active contour algorithms. Reference values were obtained with Quantra software. Inter-rater agreement for MRI measurements made with 2-mm-thick slices was excellent: for FGT-%, r = 0.994 (95% CI 0.990-0.997); for breast volume, r = 0.985 (95% CI 0.934-0.994); and for FGT volume, r = 0.979 (95% CI 0.958-0.989). MRI-based FGT-% correlated strongly with MBD in mammography (r = 0.819-0.904, P < 0.001) and moderately to high with MBD in tomosynthesis (r = 0.630-0.738, P < 0.001). K-means clustering-based assessments of the proportion of the fibroglandular tissue in the breast at MRI are highly reproducible. In the future, quantitative assessment of FGT-% to complement visual estimation of FGT should be performed on a more regular basis as it provides a component which can be incorporated into the individual's breast cancer risk stratification.
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12
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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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Lu KM, Yeh DM, Cao BH, Lin CY, Liang CY, Zhou YB, Tsai CJ. Quantitative evaluation of breast density using a dual-energy technique on a digital breast tomosynthesis system. J Appl Clin Med Phys 2019; 20:170-177. [PMID: 31106990 PMCID: PMC6560244 DOI: 10.1002/acm2.12618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Revised: 04/23/2019] [Accepted: 04/29/2019] [Indexed: 11/25/2022] Open
Abstract
Purpose Although breast density is considered a strong risk factor of breast cancer, its quantitative assessment is difficult. To investigate a quantitative method of measuring breast density using dual‐energy mammographic imaging with central digital breast tomosynthesis in physically uniform and nonuniform phantoms. Material and methods The dual‐energy imaging unit used a tungsten anode and silver filter with 30 kVp for high‐energy images and 20 kVp for low‐energy images. Uniform glandular‐equivalent phantoms were used to calibrate a dual‐energy based decomposition algorithm. The first study used uniform breast phantoms which ranged in thicknesses from 20 to 70 mm, in 10‐mm increments, and which provided 30%, 50%, and 70% of breast density. The second study used uniform phantoms ranging from 10% to 90% of breast density. The third study used non‐uniform phantoms (at an average density of 50%) with a thickness which ranged from 20 to 90 mm, in 10‐mm increments. Results The root mean square error of breast density measurements was 2.64–3.34% for the uniform, variable thickness phantoms, 4.17% for the uniform, variable density phantoms, and 4.49% for the nonuniform, variable thickness phantoms. Conclusion The dual‐energy technique could be used to measure breast density with a margin of error of < 10% using digital breast tomosynthesis.
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Affiliation(s)
- Kun-Mu Lu
- Department of Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei City, Taiwan
| | - Da-Ming Yeh
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan.,Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung City, Taiwan
| | - Bi-Hui Cao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chia-Yi Lin
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
| | - Chih-Yu Liang
- Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan.,Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City, Taiwan.,Department of Emergency Medicine, EDA Hospital, I-Shou University, Kaohsiung City, Taiwan
| | - Yu-Bo Zhou
- Department of Library, Guangzhou Medical University, Guangzhou, China
| | - Chia-Jung Tsai
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung City, Taiwan
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14
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Moshina N, Roman M, Sebuødegård S, Waade GG, Ursin G, Hofvind S. Comparison of subjective and fully automated methods for measuring mammographic density. Acta Radiol 2018; 59:154-160. [PMID: 28565960 DOI: 10.1177/0284185117712540] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (kw). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was kw = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.
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Affiliation(s)
| | | | | | - Gunvor G Waade
- Oslo and Akershus University College of Applied Sciences, Faculty of Health Science, Oslo, Norway
| | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Institute of Basic Medical Sciences, Medical Faculty, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, CA, USA
| | - Solveig Hofvind
- Cancer Registry of Norway, Oslo, Norway
- Oslo and Akershus University College of Applied Sciences, Faculty of Health Science, Oslo, Norway
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15
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The Association of Mammographic Density and Molecular Breast Cancer Subtype. Cancer Epidemiol Biomarkers Prev 2017; 26:1487-1492. [DOI: 10.1158/1055-9965.epi-16-0881] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 05/16/2017] [Accepted: 06/28/2017] [Indexed: 11/16/2022] Open
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16
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Taghanaki SA, Kawahara J, Miles B, Hamarneh G. Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 145:85-93. [PMID: 28552129 DOI: 10.1016/j.cmpb.2017.04.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 03/21/2017] [Accepted: 04/12/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). METHODS In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. RESULTS We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. CONCLUSIONS We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features.
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Affiliation(s)
| | - Jeremy Kawahara
- Medical Image Analysis Lab, Simon Fraser University, Canada.
| | - Brandon Miles
- Medical Image Analysis Lab, Simon Fraser University, Canada.
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Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad. Diagnostics (Basel) 2017; 7:diagnostics7020030. [PMID: 28561776 PMCID: PMC5489950 DOI: 10.3390/diagnostics7020030] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/22/2017] [Accepted: 05/24/2017] [Indexed: 12/14/2022] Open
Abstract
Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.
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Three-Class Mammogram Classification Based on Descriptive CNN Features. BIOMED RESEARCH INTERNATIONAL 2017; 2017:3640901. [PMID: 28191461 PMCID: PMC5274695 DOI: 10.1155/2017/3640901] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 11/07/2016] [Accepted: 11/16/2016] [Indexed: 12/02/2022]
Abstract
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.
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Li H, Weiss WA, Medved M, Abe H, Newstead GM, Karczmar GS, Giger ML. Breast density estimation from high spectral and spatial resolution MRI. J Med Imaging (Bellingham) 2017; 3:044507. [PMID: 28042590 DOI: 10.1117/1.jmi.3.4.044507] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 12/05/2016] [Indexed: 11/14/2022] Open
Abstract
A three-dimensional breast density estimation method is presented for high spectral and spatial resolution (HiSS) MR imaging. Twenty-two patients were recruited (under an Institutional Review Board--approved Health Insurance Portability and Accountability Act-compliant protocol) for high-risk breast cancer screening. Each patient received standard-of-care clinical digital x-ray mammograms and MR scans, as well as HiSS scans. The algorithm for breast density estimation includes breast mask generating, breast skin removal, and breast percentage density calculation. The inter- and intra-user variabilities of the HiSS-based density estimation were determined using correlation analysis and limits of agreement. Correlation analysis was also performed between the HiSS-based density estimation and radiologists' breast imaging-reporting and data system (BI-RADS) density ratings. A correlation coefficient of 0.91 ([Formula: see text]) was obtained between left and right breast density estimations. An interclass correlation coefficient of 0.99 ([Formula: see text]) indicated high reliability for the inter-user variability of the HiSS-based breast density estimations. A moderate correlation coefficient of 0.55 ([Formula: see text]) was observed between HiSS-based breast density estimations and radiologists' BI-RADS. In summary, an objective density estimation method using HiSS spectral data from breast MRI was developed. The high reproducibility with low inter- and low intra-user variabilities shown in this preliminary study suggest that such a HiSS-based density metric may be potentially beneficial in programs requiring breast density such as in breast cancer risk assessment and monitoring effects of therapy.
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Affiliation(s)
- Hui Li
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - William A Weiss
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - Milica Medved
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - Hiroyuki Abe
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - Gillian M Newstead
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - Gregory S Karczmar
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
| | - Maryellen L Giger
- University of Chicago , Department of Radiology, 5841 South Maryland Avenue, MC 2026, Chicago, Illinois 60637, United States
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20
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Kumar I, H.S. B, Virmani J, Thakur S. A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.01.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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21
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Bowles D, Quinton A. The Use of Ultrasound in Breast Cancer Screening of Asymptomatic Women with Dense Breast Tissue: A Narrative Review. J Med Imaging Radiat Sci 2016; 47:S21-S28. [PMID: 31047483 DOI: 10.1016/j.jmir.2016.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 06/22/2016] [Accepted: 06/24/2016] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Mammography is the standard screening modality for breast cancer; however, sensitivity reduces with increasing breast density, resulting in the potential for masking of cancer. Ultrasound is a potential supplemental screening tool, but its routine use is controversial. METHODS A database search was performed with keywords "ultrasound" and "breast density and screening", including variations. Articles were included if they assessed the use of hand-held ultrasound as a supplemental screening modality in women with dense breasts. DISCUSSION Twelve articles were identified. No high-level evidence articles were identified. Cancer detection rates increased with the addition of ultrasound-to-mammography screening protocols. However, this was associated with increased costs per cancer detected, an increased biopsy rate, and a low positive predictive value. The survival benefit, cost versus benefit, and psychological impact of the addition of ultrasound is unknown. CONCLUSIONS The addition of ultrasound to a screening program in an asymptomatic population of women with dense breast tissue detects additional cancers compared with mammography alone. Knowledge regarding a survival or cost benefit associated with increased cancer detection, and the psychological impact of the addition of ultrasound is unknown. Further research is needed to assess whether the addition of ultrasound is cost-effective with respect to clinical outcome and survival.
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Affiliation(s)
- Danielle Bowles
- Medical Sonography, School of Medical and Applied Science, CQ University, Brisbane, Queensland, Australia.
| | - Ann Quinton
- School of Medical and Applied Sciences, Sydney Campus, CQUniversity, Sydney, New South Wales, Australia
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22
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Whitman GJ, Woodtichartpreecha P. Breast Density Legislation: More Evidence is Needed. Breast J 2016; 22:491-2. [PMID: 27571966 DOI: 10.1111/tbj.12629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Gary J Whitman
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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23
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Nelson KP, Edwards D. A measure of association for ordered categorical data in population-based studies. Stat Methods Med Res 2016; 27:812-831. [PMID: 27184590 DOI: 10.1177/0962280216643347] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ordinal classification scales are commonly used to define a patient's disease status in screening and diagnostic tests such as mammography. Challenges arise in agreement studies when evaluating the association between many raters' classifications of patients' disease or health status when an ordered categorical scale is used. In this paper, we describe a population-based approach and chance-corrected measure of association to evaluate the strength of relationship between multiple raters' ordinal classifications where any number of raters can be accommodated. In contrast to Shrout and Fleiss' intraclass correlation coefficient, the proposed measure of association is invariant with respect to changes in disease prevalence. We demonstrate how unique characteristics of individual raters can be explored using random effects. Simulation studies are conducted to demonstrate the properties of the proposed method under varying assumptions. The methods are applied to two large-scale agreement studies of breast cancer screening and prostate cancer severity.
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Affiliation(s)
- Kerrie P Nelson
- 1 Department of Biostatistics, Boston University, Boston, USA
| | - Don Edwards
- 2 Department of Statistics, University of South Carolina, South Carolina, USA
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24
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Chen L, Ray S, Keller BM, Pertuz S, McDonald ES, Conant EF, Kontos D. The Impact of Acquisition Dose on Quantitative Breast Density Estimation with Digital Mammography: Results from ACRIN PA 4006. Radiology 2016; 280:693-700. [PMID: 27002418 DOI: 10.1148/radiol.2016151749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To investigate the impact of radiation dose on breast density estimation in digital mammography. Materials and Methods With institutional review board approval and Health Insurance Portability and Accountability Act compliance under waiver of consent, a cohort of women from the American College of Radiology Imaging Network Pennsylvania 4006 trial was retrospectively analyzed. All patients underwent breast screening with a combination of dose protocols, including standard full-field digital mammography, low-dose digital mammography, and digital breast tomosynthesis. A total of 5832 images from 486 women were analyzed with previously validated, fully automated software for quantitative estimation of density. Clinical Breast Imaging Reporting and Data System (BI-RADS) density assessment results were also available from the trial reports. The influence of image acquisition radiation dose on quantitative breast density estimation was investigated with analysis of variance and linear regression. Pairwise comparisons of density estimations at different dose levels were performed with Student t test. Agreement of estimation was evaluated with quartile-weighted Cohen kappa values and Bland-Altman limits of agreement. Results Radiation dose of image acquisition did not significantly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent density demonstrating a high overall correlation between protocols (r = 0.88-0.95; weighted κ = 0.83-0.90). However, differences in breast percent density (1.04% and 3.84%, P < .05) were observed within high BI-RADS density categories, although they were significantly correlated across the different acquisition dose levels (r = 0.76-0.92, P < .05). Conclusion Precision and reproducibility of automated breast density measurements with digital mammography are not substantially affected by variations in radiation dose; thus, the use of low-dose techniques for the purpose of density estimation may be feasible. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Lin Chen
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Shonket Ray
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Brad M Keller
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Said Pertuz
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Elizabeth S McDonald
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Emily F Conant
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
| | - Despina Kontos
- From the Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3600 Market St, Suite 360, Philadelphia PA 19104-2643
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Application of Statistical Texture Features for Breast Tissue Density Classification. IMAGE FEATURE DETECTORS AND DESCRIPTORS 2016. [DOI: 10.1007/978-3-319-28854-3_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2016. [DOI: 10.1007/978-3-319-21212-8_7] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Damases CN, Brennan PC, Mello-Thoms C, McEntee MF. Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists. Acad Radiol 2016; 23:70-7. [PMID: 26514436 DOI: 10.1016/j.acra.2015.09.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/31/2015] [Accepted: 09/16/2015] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate agreement on mammographic breast density (MD) assessment between automated volumetric software and Breast Imaging Reporting and Data System (BIRADS) categorization by expert radiologists. MATERIALS AND METHODS Forty cases of left craniocaudal and mediolateral oblique mammograms from 20 women were used. All images had their volumetric density classified using Volpara density grade (VDG) and average volumetric breast density percentage. The same images were then classified into BIRADS categories (I-IV) by 20 American Board of Radiology examiners. RESULTS The results demonstrated a moderate agreement (κ = 0.537; 95% CI = 0.234-0.699) between VDG classification and radiologists' BIRADS density assessment. Interreader agreement using BIRADS also demonstrated moderate agreement (κ = 0.565; 95% CI = 0.519-0.610) ranging from 0.328 to 0.669. Radiologists' average BIRADS was lower than average VDG scores by 0.33, with their mean being 2.13, whereas the mean VDG was 2.48 (U = -3.742; P < 0.001). VDG and BIRADS showed a very strong positive correlation (ρ = 0.91; P < 0.001) as did BIRADS and average volumetric breast density percentage (ρ = 0.94; P < 0.001). CONCLUSIONS Automated volumetric breast density assessment shows moderate agreement and very strong correlation with BIRADS; interreader variations still exist within BIRADS. Because of the increasing importance of MD measurement in clinical management of patients, widely accepted, reproducible, and accurate measures of MD are required.
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Affiliation(s)
- Christine N Damases
- Faculty of Health Sciences, Discipline of Medical Radiation Sciences and Brain and Mind Research Institute, M205, Cumberland Campus, The University of Sydney, 75 East St, Room M205, Lidcombe, Sydney, NSW 2141, Australia; Faculty of Health Sciences, Department of Radiography, University of Namibia, M-Block, Room M-105, Mandume Ndemufayo Avenue, Private Bag 13310, Windhoek 9000, Namibia.
| | - Patrick C Brennan
- Faculty of Health Sciences, Discipline of Medical Radiation Sciences and Brain and Mind Research Institute, M205, Cumberland Campus, The University of Sydney, 75 East St, Room M205, Lidcombe, Sydney, NSW 2141, Australia
| | - Claudia Mello-Thoms
- Faculty of Health Sciences, Discipline of Medical Radiation Sciences and Brain and Mind Research Institute, M205, Cumberland Campus, The University of Sydney, 75 East St, Room M205, Lidcombe, Sydney, NSW 2141, Australia
| | - Mark F McEntee
- Faculty of Health Sciences, Discipline of Medical Radiation Sciences and Brain and Mind Research Institute, M205, Cumberland Campus, The University of Sydney, 75 East St, Room M205, Lidcombe, Sydney, NSW 2141, Australia
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Kriti, Virmani J. Comparison of CAD Systems for Three Class Breast Tissue Density Classification Using Mammographic Images. MEDICAL IMAGING IN CLINICAL APPLICATIONS 2016. [DOI: 10.1007/978-3-319-33793-7_5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Singh T, Sharma M, Singla V, Khandelwal N. Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories. Acad Radiol 2016; 23:78-83. [PMID: 26521687 DOI: 10.1016/j.acra.2015.09.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 09/17/2015] [Accepted: 09/20/2015] [Indexed: 10/22/2022]
Abstract
RATIONALE AND OBJECTIVES The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists. MATERIALS AND METHODS A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4. RESULTS The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively). CONCLUSIONS In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography.
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Bignotti B, Ghio M, Panico N, Tagliafico G, Martinoli C, Tagliafico A. High-resolution ultrasound of peripheral nerves in systemic sclerosis: a pilot study of computer-aided quantitative assessment of nerve density. Skeletal Radiol 2015; 44:1761-7. [PMID: 26264220 DOI: 10.1007/s00256-015-2230-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Revised: 06/29/2015] [Accepted: 07/28/2015] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To quantitatively evaluate and compare nerve density in patients with limited cutaneous systemic sclerosis (lcSSc) and control subjects using high-resolution ultrasound (US) with a computer-aided assessment. METHODS Forty patients and 40 age- and sex-matched control subjects were prospectively enrolled. Ultrasound (US) examination (17-5 MHz probe) of the median nerve at the elbow was performed bilaterally by one radiologist. A software quantified the ratio between the hypoechoic and hyperechoic areas of peripheral nerves on ultrasound. Two observers set the threshold in the images acquired, and three observers performed the digital analysis of nerve density. Statistical analysis included Mann-Whitney U-test of patients versus control subjects and subgroup analysis of symptomatic and non-symptomatic patients. Intra and inter-observer agreement of the three observers were assessed with the kappa statistic. RESULTS In all, 160 median nerves were evaluated. According to the US, nerve density was significantly reduced in lcSSc patients compared to control subjects (mean and standard deviation: 41 ± 3 vs 56 ± 4, p < 0.01). Subgroup analysis showed that symptomatic patients (n = 15) had reduced nerve density compared to non-symptomatic (n = 25) patients (39 ± 5 vs 43 ± 4, p < 0.01). Intra-observer agreement was very good (K = 0.82). Inter-observer agreements were good: reader 1 vs reader 2: k = 0.78 (95% confidence interval 0.65 to 0.91); reader 2 vs reader 3: k = 0.72 (95% confidence interval 0.65 to 0.82); reader 3 vs reader 1: k = 0.71 (95% confidence interval 0.64-0.81). CONCLUSIONS In lcSSc patients, nerve density was reduced, especially in the symptomatic group, compared to control subjects.
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Affiliation(s)
- Bianca Bignotti
- Department of Health Sciences (DISSAL), University of Genoa, Largo Rosanna Benzi 8, 16132, Genoa, Italy
| | - Massimo Ghio
- Department of Internal Medicine IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Nicoletta Panico
- Department of Medicine, Lavagna Hospital, Via Don Bobbio 25, 16033, Lavagna, Italy
| | - Giulio Tagliafico
- Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Via De Marini 6, 16149, Genoa, Italy
| | - Carlo Martinoli
- Department of Health Sciences (DISSAL), University of Genoa, Largo Rosanna Benzi 8, 16132, Genoa, Italy
| | - Alberto Tagliafico
- Institute of Anatomy, Department of Experimental Medicine (DIMES), University of Genoa, Largo Rosanna Benzi, No. 8, 16132, Genoa, Italy.
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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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Tagliafico A, Bignotti B, Tagliafico G, Tosto S, Signori A, Calabrese M. Quantitative evaluation of background parenchymal enhancement (BPE) on breast MRI. A feasibility study with a semi-automatic and automatic software compared to observer-based scores. Br J Radiol 2015; 88:20150417. [PMID: 26462852 DOI: 10.1259/bjr.20150417] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate quantitative measurements of background parenchymal enhancement (BPE) on breast MRI and compare them with observer-based scores. METHODS BPE of 48 patients (mean age: 48 years; age range: 36-66 years) referred to 3.0-T breast MRI between 2012 and 2014 was evaluated independently and blindly to each other by two radiologists. BPE was estimated qualitatively with the standard Breast Imaging Reporting and Data System (BI-RADS) scale and quantitatively with a semi-automatic and an automatic software interface. To assess intrareader agreement, MRIs were re-read after a 4-month interval by the same two readers. The Pearson correlation coefficient (r) and the Bland-Altman method were used to compare the methods used to estimate BPE. p-value <0.05 was considered significant. RESULTS The mean value of BPE with the semi-automatic software evaluated by each reader was 14% (range: 2-79%) for Reader 1 and 16% (range: 1-61%) for Reader 2 (p > 0.05). Mean values of BPE percentages for the automatic software were 17.5 ± 13.1 (p > 0.05 vs semi-automatic). The automatic software was unable to produce BPE values for 2 of 48 (4%) patients. With BI-RADS, interreader and intrareader values were κ = 0.70 [95% confidence interval (CI) 0.49-0.91] and κ = 0.69 (95% CI 0.46-0.93), respectively. With semi-automated software, interreader and intrareader values were κ = 0.81 (95% CI 0.59-0.99) and κ = 0.85 (95% CI 0.43-0.99), respectively. BI-RADS scores correlated with the automatic (r = 0.55, p < 0.001) and semi-automatic scores (r = 0.60, p < 0.001). Automatic scores correlated with the semi-automatic scores (r = 0.77, p < 0.001). The mean percentage difference between automatic and semi-automatic scores was 3.5% (95% CI 1.5-5.2). CONCLUSION BPE quantitative evaluation is feasible with both semi-automatic and automatic software and correlates with radiologists' estimation. ADVANCES IN KNOWLEDGE Computerized BPE quantitative evaluation is feasible with both semi-automatic and automatic software. Computerized BPE quantitative scores correlate with radiologists' estimation.
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Affiliation(s)
- Alberto Tagliafico
- 1 Institute of Anatomy, Department of Experimental Medicine, University of Genoa, Genova, Italy
| | - Bianca Bignotti
- 2 Department of Health Sciences (DISSAL), University of Genoa, Genova, Italy
| | | | - Simona Tosto
- 4 Department of Diagnostic Senology, Ist Istituto Nazionale per la Ricerca sul Cancro, IRCCS Azienda Ospedaliera Universitaria San Martino, Genova, Italy
| | - Alessio Signori
- 2 Department of Health Sciences (DISSAL), University of Genoa, Genova, Italy
| | - Massimo Calabrese
- 4 Department of Diagnostic Senology, Ist Istituto Nazionale per la Ricerca sul Cancro, IRCCS Azienda Ospedaliera Universitaria San Martino, Genova, Italy
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Lee HN, Sohn YM, Han KH. Comparison of mammographic density estimation by Volpara software with radiologists' visual assessment: analysis of clinical-radiologic factors affecting discrepancy between them. Acta Radiol 2015; 56:1061-8. [PMID: 25338836 DOI: 10.1177/0284185114554674] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 09/17/2014] [Indexed: 01/22/2023]
Abstract
BACKGROUND Volumetric breast density analysis is useful for quantitative mammographic assessment. However, there are few studies about clinical-radiologic factors contributing to discrepancies in the visual assessment by radiologists. PURPOSE To compare automated volumetric breast density measurement with BI-RADS breast density category by radiologists' visual assessments and to evaluate the clinical-radiologic factors affecting disagreement between two estimations. MATERIAL AND METHODS From February 2011 to September 2012, 860 patients (mean age, 54.7 ± 10.2 years) who had undergone digital mammography including fully automated volumetric breast density analysis, were enrolled. The agreement in breast density assessments between two radiologists, and between an experienced radiologist and the automated software were evaluated using a weighted kappa (k) value. Clinical-radiologic factors contributing to disagreement between the results obtained by a radiologist and the automated software were evaluated using univariate and multivariate analysis. RESULTS Breast density assessments obtained by two different radiologists were in good agreement (weighted k statistics 0.835%; 95% confidence interval [CI], 0.8098-0.8608); breast density assessments obtained by an experienced radiologist versus automated software were in moderate agreement (weighted k statistics 0.799%; 95% CI, 0.7708-0.8263). Univariate analysis identified a difference in bilateral breast density and patient age as two factors that significantly contributed to disagreement between the two approaches (P = 0.0002, P = 0.019). Multivariate analysis only identified a difference in bilateral breast density as a contributing factor. CONCLUSION The automated volumetric breast density measurement showed good agreement with radiologists' assessment. The difference in bilateral breast density affected the disagreement between results from visual assessment and automated software.
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Affiliation(s)
- Han Na Lee
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Yu-Mee Sohn
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Kyung Hwa Han
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
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Li Y, Brennan PC, Lee W, Nickson C, Pietrzyk MW, Ryan EA. An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance. J Digit Imaging 2015; 28:626-32. [PMID: 26259522 DOI: 10.1007/s10278-015-9814-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73% across the 40 images, with a mean range of 35 ± 13%. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10%, 29 ± 13% and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.
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Affiliation(s)
- Yanpeng Li
- Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia.
| | - Patrick C Brennan
- Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia
| | - Warwick Lee
- Cancer Institute NSW, 8 Central Avenue, Eveleigh, NSW, 2015, Australia
| | - Carolyn Nickson
- Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia
| | | | - Elaine A Ryan
- Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia
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Gur D, Klym AH, King JL, Bandos AI, Sumkin JH. Impact of the new density reporting laws: radiologist perceptions and actual behavior. Acad Radiol 2015; 22:679-83. [PMID: 25837723 DOI: 10.1016/j.acra.2015.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 01/09/2015] [Accepted: 02/03/2015] [Indexed: 11/17/2022]
Abstract
RATIONALE AND OBJECTIVES To assess radiologists' perceptions of how the new Breast Density Notification Act (BDNA) of Pennsylvania would affect their breast density reporting and their actual reporting patterns after implementation. MATERIALS AND METHODS Under an institutional review board-approved protocol, we surveyed 21 radiologists about how they believe the new law affected their breast density reporting patterns and analyzed actual changes for 16 respondents before and after the law took effect. Three hundred consecutive reports were assessed for each radiologist before and after the effective date. The distributions of reported density Breast Imaging Reporting and Data System (BI-RADS) (1-4) were compared using a type III test in the context of an ordinal mixed model accounting for between-reader variability and adjusting for age (PROC GLIMMIX, SAS, version 9.3) using a two-sided .05 significance level. RESULTS Seventeen radiologists responded to the survey; however, one retired shortly after responding. Of the 16 respondents, 56% (nine of 16) did not favor the law, 13% (two of 16) were in favor, and 31% (five of 16) were neutral. The fraction perceived that after implementation, they rated more, equally, or less frequently breasts as scattered fibroglandular densities (BI-RADS 2) versus heterogeneously dense rating (BI-RADS 3) was 50% (eight of 16), 44% (seven of 16), and 6% (one of 16), respectively. In practice, 44% (seven of 16) performed differently than their survey answers. Fourteen of 16 radiologists increased the frequency of reported BI-RADS 2 scores after BDNA implementation with seven having statistically significant (P < .05) increases after adjusting for age differences. CONCLUSIONS Radiologists' reporting patterns changed, at least for a short duration, after the new density reporting law and for some of the radiologists in an unexpected way.
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Affiliation(s)
- David Gur
- Department of Radiology, Imaging Research, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA 15213.
| | - Amy H Klym
- Department of Radiology, Imaging Research, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA 15213
| | - Jill L King
- Department of Radiology, Imaging Research, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA 15213
| | - Andriy I Bandos
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jules H Sumkin
- Department of Radiology, Breast Imaging, Magee-Womens Hospital of UPMC, Pittsburgh, Pennsylvania
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Winkel RR, von Euler-Chelpin M, Nielsen M, Diao P, Nielsen MB, Uldall WY, Vejborg I. Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer. BMC Cancer 2015; 15:274. [PMID: 25884160 PMCID: PMC4397728 DOI: 10.1186/s12885-015-1256-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Accepted: 03/25/2015] [Indexed: 01/09/2023] Open
Abstract
Background Mammographic breast density and parenchymal patterns are well-established risk factors for breast cancer. We aimed to report inter-observer agreement on three different subjective ways of assessing mammographic density and parenchymal pattern, and secondarily to examine what potential impact reproducibility has on relative risk estimates of breast cancer. Methods This retrospective case–control study included 122 cases and 262 age- and time matched controls (765 breasts) based on a 2007 screening cohort of 14,736 women with negative screening mammograms from Bispebjerg Hospital, Copenhagen. Digitised randomized film-based mammograms were classified independently by two readers according to two radiological visual classifications (BI-RADS and Tabár) and a computerized interactive threshold technique measuring area-based percent mammographic density (denoted PMD). Kappa statistics, Intraclass Correlation Coefficient (ICC) (equivalent to weighted kappa), Pearson’s linear correlation coefficient and limits-of-agreement analysis were used to evaluate inter-observer agreement. High/low-risk agreement was also determined by defining the following categories as high-risk: BI-RADS’s D3 and D4, Tabár’s PIV and PV and the upper two quartiles (within density range) of PMD. The relative risk of breast cancer was estimated using logistic regression to calculate odds ratios (ORs) adjusted for age, which were compared between the two readers. Results Substantial inter-observer agreement was seen for BI-RADS and Tabár (κ=0.68 and 0.64) and agreement was almost perfect when ICC was calculated for the ordinal BI-RADS scale (ICC=0.88) and the continuous PMD measure (ICC=0.93). The two readers judged 5% (PMD), 10% (Tabár) and 13% (BI-RADS) of the women to different high/low-risk categories, respectively. Inter-reader variability showed different impact on the relative risk of breast cancer estimated by the two readers on a multiple-category scale, however, not on a high/low-risk scale. Tabár’s pattern IV demonstrated the highest ORs of all density patterns investigated. Conclusions Our study shows the Tabár classification has comparable inter-observer reproducibility with well tested density methods, and confirms the association between Tabár’s PIV and breast cancer. In spite of comparable high inter-observer agreement for all three methods, impact on ORs for breast cancer seems to differ according to the density scale used. Automated computerized techniques are needed to fully overcome the impact of subjectivity.
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Affiliation(s)
- Rikke Rass Winkel
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - My von Euler-Chelpin
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark.
| | - Mads Nielsen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark. .,Biomediq, Fruebjergvej 3, DK-2100, Copenhagen Ø, Denmark.
| | - Pengfei Diao
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.
| | - Michael Bachmann Nielsen
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - Wei Yao Uldall
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - Ilse Vejborg
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
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Nayate AP, Dubroff JG, Schmitt JE, Nasrallah I, Kishore R, Mankoff D, Pryma DA. Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation. AJNR Am J Neuroradiol 2015; 36:1237-44. [PMID: 25767185 DOI: 10.3174/ajnr.a4281] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 01/12/2015] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Fluorine-18 florbetapir is a recently developed β-amyloid plaque positron-emission tomography imaging agent with high sensitivity, specificity, and accuracy in the detection of moderate-to-frequent cerebral cortical β-amyloid plaque. However, the FDA has expressed concerns about the consistency of interpretation of [(18)F] florbetapir PET brain scans. We hypothesized that incorporating automated cerebral-to-whole-cerebellar standardized uptake value ratios into [(18)F] florbetapir PET brain scan interpretation would reduce this interreader variability. MATERIALS AND METHODS This randomized, blinded-reader study used previously acquired [(18)F] florbetapir scans from 30 anonymized patients who were enrolled in the Alzheimer's Disease Neuroimaging Initiative 2. In 4 separate, blinded-reading sessions, 5 readers classified 30 cases as positive or negative for significant β-amyloid deposition either qualitatively alone or qualitatively with additional adjunct software that determined standardized uptake value ratios. A κ coefficient was used to calculate interreader agreement with and without the use of standardized uptake value ratios. RESULTS There was complete interreader agreement on 20/30 cases of [(18)F] florbetapir PET brain scans by using qualitative interpretation and on 27/30 scans interpreted with the adjunct use of standardized uptake value ratios. The κ coefficient for the studies read with standardized uptake value ratios (0.92) was significantly higher compared with the qualitatively read studies (0.69, P = .006). CONCLUSIONS Use of standardized uptake value ratios improves interreader agreement in the interpretation of [(18)F] florbetapir images.
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Affiliation(s)
- A P Nayate
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - J G Dubroff
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - J E Schmitt
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - I Nasrallah
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - R Kishore
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - D Mankoff
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - D A Pryma
- From the Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania.
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Martínez Gómez I, Casals el Busto M, Antón Guirao J, Ruiz Perales F, Llobet Azpitarte R. Estimación semiautomática de la densidad mamaria con DM-Scan. RADIOLOGIA 2014; 56:429-34. [DOI: 10.1016/j.rx.2012.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Revised: 11/20/2012] [Accepted: 11/22/2012] [Indexed: 10/27/2022]
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Tagliafico AS, Tagliafico G. Fascicular ratio: a new parameter to evaluate peripheral nerve pathology on magnetic resonance imaging: a feasibility study on a 3T MRI system. Medicine (Baltimore) 2014; 93:e68. [PMID: 25255018 PMCID: PMC4616287 DOI: 10.1097/md.0000000000000068] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The objective of the study was to define and quantitatively evaluate the fascicular ratio (FR) on magnetic resonance imaging (MRI) in patients with peripheral neuropathies compared with healthy controls. Forty control subjects (20 women, 20 men; age, 44.6 ± 13.4 years) and 40 patients with peripheral neuropathy (22 women, 18 men; age, 50.3 ± 10.2 years) were examined with a standard 3T MRI protocol. With customized software (with semiautomatic and automatic interface), the hypointense and hyperintense areas of the peripheral nerves corresponding to fascicular and nonfascicular tissue were examined on T1-weighted sequences. The ratio of fascicular pixels to total pixels was called FR. Correlation with FR calculated on high-resolution ultrasound was performed. The statistical analysis included the Mann-Whitney U test of controls versus patients, the receiver operating characteristic (ROC) analysis, and the subgroup analysis of patients according to etiologies of neuropathy. Intraobserver and interobserver agreement was calculated based on the evaluation made by 3 readers. Finally, a complete automatic evaluation was performed. On MRI, FRs were significantly increased in patients compared with controls (FR, 76.7 ± 15.1 vs 56 ± 12.3; P < 0.0001 for the semiautomatic interface; and FR 66.3 ± 17.5 vs 47.8 ± 18.4; P < 0.0001 for the automatic interface). The increase in FR was caused mainly by an increase in the hypointense part of the nerve. This observation was valid for all causes of neuropathies. ROC analysis found an area under the curve of 0.75 (95% confidence interval, 0.44-0.81) for FR to discriminate neuropathy from control. The correlation coefficient between MRI and ultrasound was significant (r = 0.49; 95% confidence interval for r, 0.21-0.70; P = 0.012). With the semiautomated evaluation, the mean intraobserver agreement was good (K = 0.86). The interobserver agreements were also good (reader 1 vs reader 2, k = 0.71; reader 2 vs reader 3, k = 0.78; reader 3 vs reader 1, k = 0.71). There were no statistically significant differences between the results obtained using the 2 methods. FR calculation on MRI is feasible, and it may be used in adjunct to standard MRI evaluation in peripheral nerve disorders.
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Affiliation(s)
- Alberto S Tagliafico
- Institute of Anatomy, Department of Experimental Medicine (DIMES), University of Genoa (AST); and CNR-IMATI, Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Genova, Italy (GT)
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Martínez Gómez I, Casals el Busto M, Antón Guirao J, Ruiz Perales F, Llobet Azpitarte R. Semiautomatic estimation of breast density with DM-Scan software. RADIOLOGIA 2014. [DOI: 10.1016/j.rxeng.2012.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Ekpo EU, McEntee MF. Measurement of breast density with digital breast tomosynthesis--a systematic review. Br J Radiol 2014; 87:20140460. [PMID: 25146640 DOI: 10.1259/bjr.20140460] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54-0.94, whilst that of BI-RADs and MedDensity ranged from 0.48-0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred.
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Affiliation(s)
- E U Ekpo
- 1 Discipline of Medical Radiation Science, Faculty of Health Science, University of Sydney, Sydney, NSW, Australia
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Sohn G, Lee JW, Park SW, Park J, Woo J, Kim HJ, Shin HJ, Kim HH, Jung KH, Sung J, Lee SW, Son BH, Ahn SH. Reliability of the percent density in digital mammography with a semi-automated thresholding method. J Breast Cancer 2014; 17:174-9. [PMID: 25013440 PMCID: PMC4090321 DOI: 10.4048/jbc.2014.17.2.174] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Accepted: 03/17/2014] [Indexed: 11/30/2022] Open
Abstract
PURPOSE The reliability of the quantitative measurement of breast density with a semi-automated thresholding method (Cumulus™) has mainly been investigated with film mammograms. This study aimed to evaluate the intrarater reproducibility of percent density (PD) by Cumulus™ with digital mammograms. METHODS This study included 1,496 craniocaudal digital mammograms from the unaffected breast of breast cancer patients. One rater reviewed each mammogram and estimated the PD using the Cumulus™ method. All images were reassessed by the same rater 1 month later without reference to the previously assigned values. The repeatability of the PD was evaluated by an intraclass correlation coefficient (ICC). All patients were grouped based on their body mass index (BMI), age, family history of breast cancer, breastfeeding history and breast area (calculated with Cumulus™), and subgroup analysis for the ICC of each group was performed. All patients were categorized by their Breast Imaging Reporting and Data System (BI-RADS) density pattern, and the mean and standard deviation of the PD by each BI-RADS categories were compared. RESULTS The ICC for the PD was 0.94, indicating excellent repeatability. The discrepancy between the paired PD values ranged from 0 to 23.93, with an average of 3.90 (standard deviation=3.39). The subgroup ICCs for the PD ranged from 0.88 to 0.96, indicating excellent reliability in all subgroups regardless of patient variables. The ICCs of the PD for the high-risk (BI-RADS 3 and 4) and low-risk (BI-RADS 1 and 2) groups were 0.90 and 0.88, respectively. CONCLUSION This study suggests that PD calculated with digital mammograms has an acceptable reliability regardless of patient age, BMI, family history of breast cancer, breastfeeding history, breast size, and BI-RADS density pattern.
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Affiliation(s)
- Guiyun Sohn
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jong Won Lee
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Won Park
- Department of Radiology, Health Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jihoon Park
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jiyoung Woo
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hwa Jung Kim
- Department of Biostatistics and Clinical Epidemiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hak Hee Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung Hae Jung
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joohon Sung
- Department of Epidemiology, School of Public Health and Institution of Health and Environment, Seoul National University, Seoul, Korea
| | - Seung Wook Lee
- Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Byung Ho Son
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sei-Hyun Ahn
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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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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Huo CW, Chew GL, Britt KL, Ingman WV, Henderson MA, Hopper JL, Thompson EW. Mammographic density-a review on the current understanding of its association with breast cancer. Breast Cancer Res Treat 2014; 144:479-502. [PMID: 24615497 DOI: 10.1007/s10549-014-2901-2] [Citation(s) in RCA: 144] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 02/24/2014] [Indexed: 01/07/2023]
Abstract
There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.
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Affiliation(s)
- C W Huo
- Department of Surgery, University of Melbourne, St. Vincent's Hospital, Melbourne, Australia,
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Vállez N, Bueno G, Déniz O, Dorado J, Seoane JA, Pazos A, Pastor C. Breast density classification to reduce false positives in CADe systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 113:569-584. [PMID: 24286729 DOI: 10.1016/j.cmpb.2013.10.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 10/03/2013] [Accepted: 10/05/2013] [Indexed: 06/02/2023]
Abstract
This paper describes a novel weighted voting tree classification scheme for breast density classification. Breast parenchymal density is an important risk factor in breast cancer. Moreover, it is known that mammogram interpretation is more difficult when dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. Several classification methods have been compared and a novel hierarchical classification procedure of combined classifiers with linear discriminant analysis (LDA) is proposed as the best solution to classify the mammograms into the four BIRADS tissue classes. The classification scheme is based on 298 texture features. Statistical analysis to test the normality and homoscedasticity of the data was carried out for feature selection. Thus, only features that are influenced by the tissue type were considered. The novel classification techniques have been incorporated into a CADe system to drive the detection algorithms and tested with 1459 images. The results obtained on the 322 screen-film mammograms (SFM) of the mini-MIAS dataset show that 99.75% of samples were correctly classified. On the 1137 full-field digital mammograms (FFDM) dataset results show 91.58% agreement. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors and show that using breast tissue classification prior to lesion detection leads to an improvement of the detection results. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.
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Affiliation(s)
- Noelia Vállez
- VISILAB, Engineering School, Universidad de Castilla-La Mancha, Spain.
| | - Gloria Bueno
- VISILAB, Engineering School, Universidad de Castilla-La Mancha, Spain.
| | - Oscar Déniz
- VISILAB, Engineering School, Universidad de Castilla-La Mancha, Spain.
| | - Julián Dorado
- RNASA-IMEDIR Group, Computer School, Universidade da Coruña, Spain
| | | | - Alejandro Pazos
- RNASA-IMEDIR Group, Computer School, Universidade da Coruña, Spain
| | - Carlos Pastor
- Department of Radiology, Hospital General Universitario de Ciudad Real, Spain
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Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment. AJR Am J Roentgenol 2013; 201:692-7. [PMID: 23971465 DOI: 10.2214/ajr.12.10197] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of our study was to estimate mammographic breast density using a fully automated volumetric breast density measurement method in comparison with BI-RADS breast density categories determined by radiologists. MATERIALS AND METHODS A total of 791 full-field digital mammography examinations with standard views were evaluated by three blinded radiologists as BI-RADS density categories 1-4. For fully automated volumetric analysis, volumetric breast density was calculated with fully automated software. The volume of fibroglandular tissue, the volume of the breast, and the volumetric percentage density were provided. RESULTS The weighted overall kappa was 0.48 (moderate agreement) for the three radiologists' estimates of BI-RADS density. Pairwise comparisons of the radiologists' measurements of BI-RADS density revealed moderate to substantial agreement, with kappa values ranging from 0.51 to 0.64. There was a significant difference in mean volumetric breast density among the BI-RADS density categories, and the mean volumetric breast density increased as the BI-RADS density category increased (p<0.001). A significant positive correlation was found between BI-RADS categories and fully automated volumetric breast density (ρ=0.765, p<0.001). CONCLUSION Our study showed good correlation of the fully automated volumetric method with radiologist-assigned BI-RADS density categories. Mammographic density assessment with the fully automated volumetric method may be used to assign BI-RADS density categories.
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Tagliafico A, Tagliafico G, Houssami N. Differences in breast density assessment using mammography, tomosynthesis and MRI and their implications for practice. Br J Radiol 2013; 86:20130528. [PMID: 24167184 DOI: 10.1259/bjr.20130528] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- A Tagliafico
- Department of Experimental Medicine, University of Genoa, Genoa, Italy
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Tagliafico AS, Tagliafico G, Cavagnetto F, Calabrese M, Houssami N. Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories. Br J Radiol 2013; 86:20130255. [PMID: 24029631 DOI: 10.1259/bjr.20130255] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. METHODS Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. RESULTS The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively). CONCLUSION Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. ADVANCES IN KNOWLEDGE On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.
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Affiliation(s)
- A S Tagliafico
- Department of Experimental Medicine, University of Genova, Genova, Italy
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Comparative estimation of percentage breast tissue density for digital mammography, digital breast tomosynthesis, and magnetic resonance imaging. Breast Cancer Res Treat 2013; 138:311-7. [PMID: 23338763 DOI: 10.1007/s10549-013-2419-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Accepted: 01/12/2013] [Indexed: 10/27/2022]
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