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Mao X, He W, Humphreys K, Eriksson M, Holowko N, Yang H, Tapia J, Hall P, Czene K. Breast Cancer Incidence After a False-Positive Mammography Result. JAMA Oncol 2024; 10:63-70. [PMID: 37917078 PMCID: PMC10623302 DOI: 10.1001/jamaoncol.2023.4519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/26/2023] [Indexed: 11/03/2023]
Abstract
Importance False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear. Objectives To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result. Design, Setting, and Participants This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023. Exposure A false-positive mammography result. Main Outcomes and Measures Breast cancer incidence and mortality. Results The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15). Conclusions and Relevance This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Research Institute, the Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Haomin Yang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - José Tapia
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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2
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Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
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3
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Mao X, He W, Humphreys K, Eriksson M, Holowko N, Strand F, Hall P, Czene K. Factors Associated With False-Positive Recalls in Mammography Screening. J Natl Compr Canc Netw 2023; 21:143-152.e4. [PMID: 36791753 DOI: 10.6004/jnccn.2022.7081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 09/27/2022] [Indexed: 02/17/2023]
Abstract
BACKGROUND We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls. METHODS We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls. RESULTS Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls. CONCLUSIONS We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Chronic Disease Research Institute, the Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Strand
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Chalfant JS, Hoyt AC. Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications. JOURNAL OF BREAST IMAGING 2022; 4:357-370. [PMID: 38416979 DOI: 10.1093/jbi/wbac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Indexed: 03/01/2024]
Abstract
Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.
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Affiliation(s)
- James S Chalfant
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
| | - Anne C Hoyt
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
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Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms. J Digit Imaging 2022; 35:910-922. [PMID: 35262841 PMCID: PMC9485387 DOI: 10.1007/s10278-019-00266-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
This study performed and assessed a novel program to improve the accuracy of short-term breast cancer risk prediction by using information from craniocaudal (CC) and mediolateral-oblique (MLO) views of two breasts. An age-matched dataset of 556 patients with at least two sequential full-field digital mammography examinations was applied. In the second examination, 278 cases were diagnosed and pathologically verified as cancer, and 278 were negative, while all cases in the first examination were negative (not recalled). Two generalized linear-model-based risk prediction models were established with global- and local-based bilateral asymmetry features for CC and MLO views first. Then, a new fusion risk model was developed by fusing prediction results of the CC- and MLO-based risk models with an adaptive alpha-integration-based fusion method. The AUC of the fusion risk model was 0.72 ± 0.02, which was significantly higher than the AUC of CC- or MLO-based risk model (P < 0.05). The maximum odds ratio for CC- and MLO-based risk models were 8.09 and 5.25, respectively, and increased to 11.99 for the fusion risk model. For subgroups of patients aged 37-49 years, 50-65 years, and 66-87 years, the AUCs of 0.73, 0.71, and 0.75 for the fusion risk model were higher than AUC for CC- and MLO-based risk models. For the BIRADS 2 and 3 subgroups, the AUC values were 0.72 and 0.71 respectively for the fusion risk model which were higher than the AUC for the CC- and MLO-based risk models. This study demonstrated that the fusion risk model we established could effectively derive and integrate supplementary and useful information extracted from both CC and MLO view images and adaptively fuse them to increase the predictive power of the short-term breast cancer risk assessment model.
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6
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Prokop J, Maršálek P, Sengul I, Pelikán A, Janoutová J, Horyl P, Roman J, Sengul D, Junior JMS. Evaluation of breast stiffness pathology based on breast compression during mammography: Proposal for novel breast stiffness scale classification. Clinics (Sao Paulo) 2022; 77:100100. [PMID: 36137345 PMCID: PMC9493386 DOI: 10.1016/j.clinsp.2022.100100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/27/2022] [Accepted: 07/13/2022] [Indexed: 11/16/2022] Open
Abstract
Breast cancer is diagnosed through a patient's Breast Self-Examination (BSE), Clinical Breast Examination (CBE), or para-clinical methods. False negativity of PCM in breast cancer diagnostics leads to a persisting problem associated with breast tumors diagnosed only in advanced stages. As the tumor volume/size at which it becomes invasive is not clear, BSE and CBE play an exceedingly important role in the early diagnosis of breast cancer. The quality and effectiveness of BSE and CBE depend on several factors, among which breast stiffness is the most important one. In this study, the authors present four methods for evaluating breast stiffness pathology during mammography examination based on the outputs obtained during the breast compression process, id est, without exposing the patient to X-Ray radiation. Based on the subjective assessment of breast stiffness by experienced medical examiners, a novel breast stiffness classification was designed, and the best method of its objective measurement was calibrated to fit the scale. Hence, this study provides an objective tool for the identification of patients who, being unable to perform valid BSE, could benefit from an increased frequency of mammography screening. Dum vivimus servimus.
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Affiliation(s)
- Jiří Prokop
- Department of Epidemiology and Public Health, Faculty of Medicine, University of Ostrava, Czechia; Department of Surgery, University Hospital Ostrava, Czechia; Department of Surgical Studies, Faculty of Medicine, University of Ostrava, Czechia
| | - Pavel Maršálek
- Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB-Technical University of Ostrava, Czechia
| | - Ilker Sengul
- Division of Endocrine Surgery, Faculty of Medicine, Giresun University, Turkey; Department of General Surgery, Faculty of Medicine, Giresun University, Turkey.
| | - Anton Pelikán
- Department of Surgery, University Hospital Ostrava, Czechia; Department of Surgical Studies, Faculty of Medicine, University of Ostrava, Czechia; Department of Health Care Sciences, Faculty of Humanities, Tomas Bata University in Zlin, Czechia
| | - Jana Janoutová
- Department of Public Health, Faculty of Medicine and Dentistry, Palacký University Olomouc, Czechia
| | - Petr Horyl
- Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB-Technical University of Ostrava, Czechia
| | - Jan Roman
- Department of Surgery, University Hospital Ostrava, Czechia; Department of Surgical Studies, Faculty of Medicine, University of Ostrava, Czechia
| | - Demet Sengul
- Department of Pathology, Faculty of Medicine, Giresun University, Turkey
| | - José Maria Soares Junior
- Universidade Federal de São Paulo, Faculdade de Medicina, Hospital das Clínicas, Departamento de Obstetrícia e Ginecologia, Disciplina de Ginecologia São Paulo (SP), Brasil
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7
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Yan S, Wang Y, Aghaei F, Qiu Y, Zheng B. Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment. Acad Radiol 2022; 29 Suppl 1:S199-S210. [PMID: 28985925 PMCID: PMC5882616 DOI: 10.1016/j.acra.2017.08.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 07/22/2017] [Accepted: 08/07/2017] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme. MATERIALS AND METHODS The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases. RESULTS With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05). CONCLUSION This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.
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Affiliation(s)
- Shiju Yan
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Yunzhi Wang
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Faranak Aghaei
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019, USA
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8
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Padilla A, Arponen O, Rinta-Kiikka I, Pertuz S. Image retrieval-based parenchymal analysis for breast cancer risk assessment. Med Phys 2021; 49:1055-1064. [PMID: 34837254 DOI: 10.1002/mp.15378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/25/2021] [Accepted: 11/08/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This research on breast cancer risk assessment aims to develop models that predict the likelihood of breast cancer. In recent years, the computerized analysis of visual texture patterns in mammograms, namely parenchymal analysis, has shown great potential for risk assessment. However, the visual complexity and heterogeneity of visual patterns limit the performance of parenchymal analysis in large populations. In this work, we propose a method to create individualized risk assessment models based on the radiological visual appearance (radiomic phenotypes) of the mammograms. METHODS We developed a content-based image retrieval system to stratify mammographic analysis according to the similarities of their radiomic phenotypes. We collected 1144 mammograms from 286 women following a case-control study design. We compared the classical parenchymal analysis with the proposed approach using the area under the ROC curve (AUC) with 95% confidence intervals (CI). Statistical significance was assessed using DeLong's test ( p < 0.05). RESULTS At a patient level, AUC values of 0.504 (95% CI: 0.398-0.611) with classical parenchymal analysis increased to 0.813 (95% CI: 0.734-0.892) when the radiomic phenotypes are incorporated with the proposed method. In risk estimation from individual, standard mammographic views, the highest performance was obtained with the mediolateral oblique view of the right breast (RMLO), with an AUC value of 0.727 (95% CI: 0.634-0.820). Differences in performance among views were statistically significant ( p < 0.05 ) CONCLUSIONS: These results indicate that the utilization of radiomic phenotypes increases the performance of computerized risk assessment based on parenchymal analysis of mammographic images. SIGNIFICANCE The creation of individualized risk assessment models may be leveraged to target personalized screening and prevention recommendations according to the person's risk.
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Affiliation(s)
- Astrid Padilla
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
| | - Otso Arponen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, 33100, Finland.,Department of Radiology, Tampere University Hospital, Tampere, 33520, Finland
| | - Irina Rinta-Kiikka
- Faculty of Medicine and Health Technology, Tampere University, Tampere, 33100, Finland.,Department of Radiology, Tampere University Hospital, Tampere, 33520, Finland
| | - Said Pertuz
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
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9
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Gastounioti A, Pantalone L, Scott CG, Cohen EA, Wu FF, Winham SJ, Jensen MR, Maidment ADA, Vachon CM, Conant EF, Kontos D. Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis. Radiology 2021; 301:561-568. [PMID: 34519572 PMCID: PMC8608738 DOI: 10.1148/radiol.2021210190] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.
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Affiliation(s)
- Aimilia Gastounioti
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Lauren Pantalone
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Christopher G Scott
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Eric A Cohen
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Fang F Wu
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Stacey J Winham
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Matthew R Jensen
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Andrew D A Maidment
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Celine M Vachon
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Emily F Conant
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Despina Kontos
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
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10
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Warner ET, Rice MS, Zeleznik OA, Fowler EE, Murthy D, Vachon CM, Bertrand KA, Rosner BA, Heine J, Tamimi RM. Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study. NPJ Breast Cancer 2021; 7:68. [PMID: 34059687 PMCID: PMC8166859 DOI: 10.1038/s41523-021-00272-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.
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Affiliation(s)
- Erica T Warner
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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11
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Valencia-Hernandez I, Peregrina-Barreto H, Reyes-Garcia CA, Lopez-Armas GC. Density map and fuzzy classification for breast density by using BI-RADS. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105825. [PMID: 33190944 DOI: 10.1016/j.cmpb.2020.105825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/29/2020] [Indexed: 06/11/2023]
Abstract
Mammographic density (MD) is conformed by a different percentage of stromal, epithelial, and adipose tissue within the breast. One of the most critical findings in mammographic patterns for establishing a diagnosis of breast cancer is high breast tissue density. There is a wide variety of works focused on the study and automatic calculation of general breast density; however, they do not provide more detailed information about the changes that may occur within the breast tissue. This work proposes to generate a breast density map based on a texture analysis to identify the internal composition and distribution of the breast tissue through the diffuse division technique of the different densities inside the breast. Therefore, it is possible to obtain a density map associated with the breast that allows us to distinguish and quantify the different types of breast densities and their distribution according to the Breast Imaging Reporting and Data System (BI-RADS Breast Density Category). The proposed methodology was tested with mammograms from the BCDR and InBreast databases, demonstrating consistency in results and reaching an accuracy of 84.2% and 81.3%, respectively. Finally, the information obtained from the density map and its analysis could be a support tool for the specialist physician to monitor changes in breast density over time, since the fuzzy classification carried out allows quantifying the degree of membership in the BI-RADS breast density classes.
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Affiliation(s)
- I Valencia-Hernandez
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México
| | - H Peregrina-Barreto
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México.
| | - C A Reyes-Garcia
- Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, Puebla 72840, México
| | - G C Lopez-Armas
- Centro de Enseñanza Técnica Industrial, Nueva Escocia 1885, Guadalajara, Jalisco, 44638, México
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12
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13
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Lian J, Li K. A Review of Breast Density Implications and Breast Cancer Screening. Clin Breast Cancer 2020; 20:283-290. [DOI: 10.1016/j.clbc.2020.03.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 02/10/2020] [Accepted: 03/12/2020] [Indexed: 12/15/2022]
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14
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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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15
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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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16
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Ho PJ, Lau HSH, Ho WK, Wong FY, Yang Q, Tan KW, Tan MH, Chay WY, Chia KS, Hartman M, Li J. Incidence of breast cancer attributable to breast density, modifiable and non-modifiable breast cancer risk factors in Singapore. Sci Rep 2020; 10:503. [PMID: 31949192 PMCID: PMC6965174 DOI: 10.1038/s41598-019-57341-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/23/2019] [Indexed: 01/08/2023] Open
Abstract
Incidence of breast cancer is rising rapidly in Asia. Some breast cancer risk factors are modifiable. We examined the impact of known breast cancer risk factors, including body mass index (BMI), reproductive and hormonal risk factors, and breast density on the incidence of breast cancer, in Singapore. The study population was a population-based prospective trial of screening mammography - Singapore Breast Cancer Screening Project. Population attributable risk and absolute risks of breast cancer due to various risk factors were calculated. Among 28,130 women, 474 women (1.7%) developed breast cancer. The population attributable risk was highest for ethnicity (49.4%) and lowest for family history of breast cancer (3.8%). The proportion of breast cancers that is attributable to modifiable risk factor BMI was 16.2%. The proportion of breast cancers that is attributable to reproductive risk factors were low; 9.2% for age at menarche and 4.2% for number of live births. Up to 45.9% of all breast cancers could be avoided if all women had breast density <12% and BMI <25 kg/m2. Notably, sixty percent of women with the lowest risk based on non-modifiable risk factors will never reach the risk level recommended for mammography screening. A combination of easily assessable breast cancer risk factors can help to identify women at high risk of developing breast cancer for targeted screening. A large number of high-risk women could benefit from risk-reduction and risk stratification strategies.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Hannah Si Hui Lau
- Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.,Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Weang Kee Ho
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham Malaysia, Selangor, Malaysia.,Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Fuh Yong Wong
- National Cancer Centre Singapore, Singapore, Singapore
| | - Qian Yang
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Ken Wei Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Min-Han Tan
- National Cancer Centre Singapore, Singapore, Singapore.,Institute of Bioengineering and Nanotechnology, Singapore, Singapore
| | - Wen Yee Chay
- National Cancer Centre Singapore, Singapore, Singapore
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore. .,Department of Surgery, Yong Loo Lin School of Medicine National University of Singapore, Singapore, Singapore.
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17
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Deng H, Li‐Tsang CWP, Li J. Measuring vascularity of hypertrophic scars by dermoscopy: Construct validity and predictive ability of scar thickness change. Skin Res Technol 2020; 26:369-375. [DOI: 10.1111/srt.12812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 11/09/2019] [Indexed: 10/25/2022]
Affiliation(s)
- Huan Deng
- Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong China
| | - Cecilia W. P. Li‐Tsang
- Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong China
| | - Jingbo Li
- Department of Burns Rehabilitation The Guangdong Provincial Work Injury Rehabilitation Hospital Guangzhou China
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18
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Pérez-Benito FJ, Signol F, Pérez-Cortés JC, Pollán M, Pérez-Gómez B, Salas-Trejo D, Casals M, Martínez I, LLobet R. Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:123-132. [PMID: 31319940 DOI: 10.1016/j.cmpb.2019.05.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/30/2019] [Accepted: 05/21/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. METHODS A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. RESULTS The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. CONCLUSION Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.
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Affiliation(s)
| | - Francois Signol
- Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera, s/n, València, 46022 Spain.
| | - Juan-Carlos Pérez-Cortés
- Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera, s/n, València, 46022 Spain.
| | - Marina Pollán
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos, 5, Madrid, 28029 Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos, 5, Madrid, 28029 Spain.
| | - Beatriz Pérez-Gómez
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de lemos, 5, Madrid, 28029 Spain; Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Carlos III Institute of Health, Monforte de Lemos, 5, Madrid, 28029 Spain.
| | - Dolores Salas-Trejo
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - María Casals
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, València, Spain; Centro Superior de Investigación en Salud Pública CSISP, FISABIO, València, Spain.
| | - Rafael LLobet
- Institute of Computer Technology, Universitat Politècnica de València, Camino de Vera, s/n, València, 46022 Spain.
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19
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Hashim HA, Mahmoud MZ, Alonazi B, Aldosary H, Alrashdi JS, Alabdulrazaq FA, Almowalad AH. Brightness Mode and Color Doppler Ultrasound in Differential Diagnosis of Breast Lesions in Saudi Females. J Clin Imaging Sci 2019; 9:36. [PMID: 31538034 PMCID: PMC6737446 DOI: 10.25259/jcis_51_2019] [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: 06/01/2019] [Accepted: 06/15/2019] [Indexed: 11/22/2022] Open
Abstract
Objective: The aim of the study was to identify the pathological characteristics of benign and malignant breast lesions among Saudi females using brightness mode (B-mode) and color Doppler ultrasound (US). Materials and Methods: This study was retrospectively carried out in a single center in the Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia. A convenient method of sampling was used to include all patients referred for different diagnosis during the period of January 2016 and December 2018. A sample size of 100 cases was selected with 50% of the cases being benign breast lesions, while the rest were malignant. The data collection instruments comprised data collection sheets, while a Philips US system with a 9 MHz linear probe was used to give the differential results. The results were considered significant when P < 0.05. The statistical diagnostic test was used to detect sensitivity, specificity, and accuracy of US in the differential diagnosis of breast lesions in Saudi females. Results: B-mode and color Doppler US findings of breast mass measurements, shape, echotexture, and the presence and absence of vascularity present a sensitivity, specificity, and accuracy of 97.09%, 80.65%, and 93.28% in the diagnosis of benign and malignant breast masses. Conclusion: In Saudi females with dense breasts, the risk of breast cancer development is increased. Moreover, B-mode in combination with color Doppler US was highly determined the results of differential diagnosis for any breast lesions.
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Affiliation(s)
- Hashim A. Hashim
- Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia,
| | - Mustafa Z. Mahmoud
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Batil Alonazi
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Hassan Aldosary
- Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia,
| | - Jameelah S. Alrashdi
- Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia,
| | - Fahad A. Alabdulrazaq
- Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia,
| | - Anood H. Almowalad
- Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia,
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20
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Ho PJ, Bok CM, Ishak HMM, Lim LY, Liu J, Wong FY, Chia KS, Tan MH, Chay WY, Hartman M, Li J. Factors associated with false-positive mammography at first screen in an Asian population. PLoS One 2019; 14:e0213615. [PMID: 30856210 PMCID: PMC6411141 DOI: 10.1371/journal.pone.0213615] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 02/25/2019] [Indexed: 11/19/2022] Open
Abstract
Introduction False-positive recall is an issue in national screening programmes. The aim of this study is to investigate the recall rate at first screen and to identify potential predictors of false-positive recall in a multi-ethnic Asian population-based breast cancer screening programme. Methods Women aged 50–64 years attending screening mammography for the first time (n = 25,318) were included in this study. The associations between potential predictors (sociodemographic, lifestyle and reproductive) and false-positive recall were evaluated using multivariable logistic regression models. Results The recall rate was 7.6% (n = 1,923), of which with 93.8% were false-positive. Factors independently associated with higher false-positive recall included Indian ethnicity (odds ratio [95% confidence interval]: 1.52 [1.25 to 1.84]), premenopause (1.23 [1.04 to 1.44]), nulliparity (1.85 [1.57 to 2.17]), recent breast symptoms (1.72 [1.31 to 2.23]) and history of breast lump excision (1.87 [1.53 to 2.26]). Factors associated with lower risk of false-positive recall included older age at screen (0.84 [0.73 to 0.97]) and use of oral contraceptives (0.87 [0.78 to 0.97]). After further adjustment of percent mammographic density, associations with older age at screening (0.97 [0.84 to 1.11]) and menopausal status (1.12 [0.95 to 1.32]) were attenuated and no longer significant. Conclusion For every breast cancer identified, 15 women without cancer were subjected to further testing. Efforts to educate Asian women on what it means to be recalled will be useful in reducing unnecessary stress and anxiety.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore, Genome, Singapore, Singapore, Singapore
| | - Chek Mei Bok
- Genome Institute of Singapore, Genome, Singapore, Singapore, Singapore
| | | | - Li Yan Lim
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Jenny Liu
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | | | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Min-Han Tan
- National Cancer Centre, Singapore, Singapore
- Institute of Bioengineering and Nanotechnology, Singapore, Singapore
| | | | - Mikael Hartman
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Jingmei Li
- Genome Institute of Singapore, Genome, Singapore, Singapore, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
- Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden
- * E-mail:
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21
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Alomaim W, O'Leary D, Ryan J, Rainford L, Evanoff M, Foley S. Variability of Breast Density Classification Between US and UK Radiologists. J Med Imaging Radiat Sci 2019; 50:53-61. [DOI: 10.1016/j.jmir.2018.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 06/09/2018] [Accepted: 11/27/2018] [Indexed: 12/22/2022]
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23
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Li J, Ugalde-Morales E, Wen WX, Decker B, Eriksson M, Torstensson A, Christensen HN, Dunning AM, Allen J, Luccarini C, Pooley KA, Simard J, Dorling L, Easton DF, Teo SH, Hall P, Czene K. Differential Burden of Rare and Common Variants on Tumor Characteristics, Survival, and Mode of Detection in Breast Cancer. Cancer Res 2018; 78:6329-6338. [PMID: 30385609 DOI: 10.1158/0008-5472.can-18-1018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/25/2018] [Accepted: 09/26/2018] [Indexed: 11/16/2022]
Abstract
Genetic variants that increase breast cancer risk can be rare or common. This study tests whether the genetic risk stratification of breast cancer by rare and common variants in established loci can discriminate tumors with different biology, patient survival, and mode of detection. Multinomial logistic regression tested associations between genetic risk load [protein-truncating variant (PTV) carriership in 31 breast cancer predisposition genes-or polygenic risk score (PRS) using 162 single-nucleotide polymorphisms], tumor characteristics, and mode of detection (OR). Ten-year breast cancer-specific survival (HR) was estimated using Cox regression models. In this unselected cohort of 5,099 patients with breast cancer diagnosed in Sweden between 2001 and 2008, PTV carriers (n = 597) were younger and associated with more aggressive tumor phenotypes (ER-negative, large size, high grade, high proliferation, luminal B, and basal-like subtype) and worse outcome (HR, 1.65; 1.16-2.36) than noncarriers. After excluding 92 BRCA1/2 carriers, PTV carriership remained associated with high grade and worse survival (HR, 1.76; 1.21-2.56). In 5,007 BRCA1/2 noncarriers, higher PRS was associated with less aggressive tumor characteristics (ER-positive, PR-positive, small size, low grade, low proliferation, and luminal A subtype). Among patients with low mammographic density (<25%), non-BRCA1/2 PTV carriers were more often interval than screen-detected breast cancer (OR, 1.89; 1.12-3.21) than noncarriers. In contrast, higher PRS was associated with lower risk of interval compared with screen-detected cancer (OR, 0.77; 0.64-0.93) in women with low mammographic density. These findings suggest that rare and common breast cancer susceptibility loci are differentially associated with tumor characteristics, survival, and mode of detection.Significance: These findings offer the potential to improve screening practices for breast cancer by providing a deeper understanding of how risk variants affect disease progression and mode of detection. Cancer Res; 78(21); 6329-38. ©2018 AACR.
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Affiliation(s)
- Jingmei Li
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Emilio Ugalde-Morales
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei Xiong Wen
- Cancer Research Malaysia, Sime Darby Medical Centre, Selangor, Subang Jaya, Malaysia
| | - Brennan Decker
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Karen A Pooley
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Canada Research Chair in Oncogenetics, Université Laval, Quebec City, Canada
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Soo Hwang Teo
- Cancer Research Malaysia, Sime Darby Medical Centre, Selangor, Subang Jaya, Malaysia
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Strand F, Humphreys K, Holm J, Eriksson M, Törnberg S, Hall P, Azavedo E, Czene K. Long-term prognostic implications of risk factors associated with tumor size: a case study of women regularly attending screening. Breast Cancer Res 2018; 20:31. [PMID: 29669579 PMCID: PMC5907386 DOI: 10.1186/s13058-018-0962-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 03/21/2018] [Indexed: 11/25/2022] Open
Abstract
Background Breast cancer prognosis is strongly associated with tumor size at diagnosis. We aimed to identify factors associated with diagnosis of large (> 2 cm) compared to small tumors, and to examine implications for long-term prognosis. Methods We examined 2012 women with invasive breast cancer, of whom 1466 had screen-detected and 546 interval cancers that were incident between 2001 and 2008 in a population-based screening cohort, and followed them to 31 December 2015. Body mass index (BMI) was ascertained after diagnosis at the time of study enrollment during 2009. PD was measured based on the contralateral mammogram within 3 years before diagnosis. We used multiple logistic regression modeling to examine the association between tumor size and body mass index (BMI), mammographic percent density (PD), or hormonal and genetic risk factors. Associations between the identified risk factors and, in turn, the outcomes of local recurrence, distant metastases, and death (153 events in total) in women with breast cancer were examined using Cox regression. Analyses were carried out according to mode of detection. Results BMI and PD were the only factors associated with tumor size at diagnosis. For BMI (≥25 vs. < 25 kg/m2), the multiple adjusted odds ratios (OR) were 1.37 (95% CI 1.02–1.83) and 2.12 (95% CI 1.41–3.18), for screen-detected and interval cancers, respectively. For PD (≥20 vs. < 20%), the corresponding ORs were 1.72 (95% CI 1.29–2.30) and 0.60 (95% CI 0.40–0.90). Among women with interval cancers, those with high BMI had worse prognosis than women with low BMI (hazard ratio 1.70; 95% CI 1.04–2.77), but PD was not associated with the hazard rate. Among screen-detected cancers, neither BMI nor PD was associated with the hazard rate. Conclusions In conclusion, high BMI was associated with the risk of having a tumor larger than 2 cm at diagnosis. Among women with interval cancer, high BMI was associated with worse prognosis. We believe that women with high BMI should be especially encouraged to attend screening. Electronic supplementary material The online version of this article (10.1186/s13058-018-0962-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fredrik Strand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden. .,Thoracic Radiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Johanna Holm
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
| | - Sven Törnberg
- Department of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
| | - Edward Azavedo
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden
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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 2018; 30:215-227. [PMID: 27832519 DOI: 10.1007/s10278-016-9922-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.
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26
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McLean KE, Stone J. Role of breast density measurement in screening for breast cancer. Climacteric 2018; 21:214-220. [DOI: 10.1080/13697137.2018.1424816] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- K. E. McLean
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
| | - J. Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
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27
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A comprehensive tool for measuring mammographic density changes over time. Breast Cancer Res Treat 2018; 169:371-379. [PMID: 29392583 PMCID: PMC5945741 DOI: 10.1007/s10549-018-4690-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/21/2018] [Indexed: 11/14/2022]
Abstract
Background Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Method Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. Results The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3–1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60–0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. Conclusions The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up. Electronic supplementary material The online version of this article (10.1007/s10549-018-4690-5) contains supplementary material, which is available to authorized users.
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28
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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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29
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Lim LY, Ho PJ, Liu J, Chay WY, Tan MH, Hartman M, Li J. Determinants of breast size in Asian women. Sci Rep 2018; 8:1201. [PMID: 29352164 PMCID: PMC5775321 DOI: 10.1038/s41598-018-19437-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 01/02/2018] [Indexed: 12/26/2022] Open
Abstract
Breast size as a risk factor of breast cancer has been studied extensively with inconclusive results. Here we examined the associations between breast size and breast cancer risk factors in 24,353 Asian women aged 50 to 64 years old enrolled in a nationwide mammography screening project conducted between October 1994 and February 1997. Information on demographic and reproductive factors was obtained via a questionnaire. Breast size was ascertained as bust line measured at study recruitment and total breast area measured from a mammogram. The average bust line and total breast area was 91.2 cm and 102.3 cm2, respectively. The two breast measurements were moderately correlated (Spearman correlation coefficient = 0.65). Age, BMI, marital and working status were independently associated with bust line and total breast area. In the multivariable analyses, the most pronounced effects were observed for BMI (24.2 cm difference in bust line and 39.4 cm2 in breast area comparing women with BMI ≥30 kg/m2 to BMI <20 kg/m2). Ethnicity was a positive predictor for total breast area, but not bust line.
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Affiliation(s)
- Li Yan Lim
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Peh Joo Ho
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jenny Liu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | | | - Min-Han Tan
- National Cancer Centre, Singapore, Singapore.,Institute of Bioengineering and Nanotechnology, Singapore, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. .,Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
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30
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Li Y, Fan M, Cheng H, Zhang P, Zheng B, Li L. Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk. Phys Med Biol 2018; 63:025004. [PMID: 29226849 DOI: 10.1088/1361-6560/aaa096] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This study aims to develop and test a new imaging marker-based short-term breast cancer risk prediction model. An age-matched dataset of 566 screening mammography cases was used. All 'prior' images acquired in the two screening series were negative, while in the 'current' screening images, 283 cases were positive for cancer and 283 cases remained negative. For each case, two bilateral cranio-caudal view mammograms acquired from the 'prior' negative screenings were selected and processed by a computer-aided image processing scheme, which segmented the entire breast area into nine strip-based local regions, extracted the element regions using difference of Gaussian filters, and computed both global- and local-based bilateral asymmetrical image features. An initial feature pool included 190 features related to the spatial distribution and structural similarity of grayscale values, as well as of the magnitude and phase responses of multidirectional Gabor filters. Next, a short-term breast cancer risk prediction model based on a generalized linear model was built using an embedded stepwise regression analysis method to select features and a leave-one-case-out cross-validation method to predict the likelihood of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) values significantly increased from 0.5863 ± 0.0237 to 0.6870 ± 0.0220 when the model trained by the image features extracted from the global regions and by the features extracted from both the global and the matched local regions (p = 0.0001). The odds ratio values monotonically increased from 1.00-8.11 with a significantly increasing trend in slope (p = 0.0028) as the model-generated risk score increased. In addition, the AUC values were 0.6555 ± 0.0437, 0.6958 ± 0.0290, and 0.7054 ± 0.0529 for the three age groups of 37-49, 50-65, and 66-87 years old, respectively. AUC values of 0.6529 ± 0.1100, 0.6820 ± 0.0353, 0.6836 ± 0.0302 and 0.8043 ± 0.1067 were yielded for the four mammography density sub-groups (BIRADS from 1-4), respectively. This study demonstrated that bilateral asymmetry features extracted from local regions combined with the global region in bilateral negative mammograms could be used as a new imaging marker to assist in the prediction of short-term breast cancer risk.
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Affiliation(s)
- Yane Li
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
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31
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Isheden G, Humphreys K. Modelling breast cancer tumour growth for a stable disease population. Stat Methods Med Res 2017; 28:681-702. [DOI: 10.1177/0962280217734583] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.
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Affiliation(s)
- Gabriel Isheden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
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32
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Andersson TML, Crowther MJ, Czene K, Hall P, Humphreys K. Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach. Am J Epidemiol 2017. [PMID: 28633324 PMCID: PMC5860633 DOI: 10.1093/aje/kwx178] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50–74 years, diagnosed with breast cancer in Sweden during 1993–1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen.
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Affiliation(s)
- Therese M -L Andersson
- Correspondence to Dr. Therese M.-L. Andersson, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, SE-17177 Stockholm, Sweden (e-mail: )
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Wang C, Brentnall AR, Cuzick J, Harkness EF, Evans DG, Astley S. A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies. Breast Cancer Res 2017; 19:114. [PMID: 29047382 PMCID: PMC5648465 DOI: 10.1186/s13058-017-0906-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 09/27/2017] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw full field digital mammograms (FFDM). METHOD A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 cases, 931 controls). Predictive ability was assessed using deviance and matched concordance index (mC). The ability to improve risk estimation beyond percent volumetric density (Volpara) was evaluated using conditional logistic regression. RESULTS The strongest features identified in the training set were "sum average" based on the grey-level co-occurrence matrix at low image resolutions (original resolution 10.628 pixels per mm; downsized by factors of 16, 32 and 64), which had a better deviance and mC than volumetric PD. In the validation study, the risk score combining the three sum average features achieved a better deviance than volumetric PD (Δχ2 = 10.55 or 6.95 if logarithm PD) and a similar mC to volumetric PD (0.58 and 0.57, respectively). The risk score added independent information to volumetric PD (Δχ2 = 14.38, p = 0.0008). CONCLUSION Textural features based on digital mammograms improve risk assessment beyond volumetric percentage density. The features and risk score developed need further investigation in other settings.
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Affiliation(s)
- Chao Wang
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Adam R. Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Elaine F. Harkness
- Centre for Imaging Science, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT UK
| | - D. Gareth Evans
- Department of Genomic Medicine, University of Manchester, St Mary’s Hospital, Manchester, M13 9WL UK
| | - Susan Astley
- Centre for Imaging Science, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT UK
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Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction. Int J Comput Assist Radiol Surg 2017; 12:1819-1828. [PMID: 28726117 DOI: 10.1007/s11548-017-1648-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 07/12/2017] [Indexed: 10/19/2022]
Abstract
PURPOSE How to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction. METHODS A dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method. RESULTS By using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830 ± 0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633 ± 0.043 and 57.1% achieved by using the previous density segmentation method ([Formula: see text], t-test). CONCLUSIONS A new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.
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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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Holm J, Eriksson L, Ploner A, Eriksson M, Rantalainen M, Li J, Hall P, Czene K. Assessment of Breast Cancer Risk Factors Reveals Subtype Heterogeneity. Cancer Res 2017; 77:3708-3717. [PMID: 28512241 DOI: 10.1158/0008-5472.can-16-2574] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 02/05/2017] [Accepted: 04/19/2017] [Indexed: 11/16/2022]
Abstract
Subtype heterogeneity for breast cancer risk factors has been suspected, potentially reflecting etiologic differences and implicating risk prediction. However, reports are conflicting regarding the presence of heterogeneity for many exposures. To examine subtype heterogeneity across known breast cancer risk factors, we conducted a case-control analysis of 2,632 breast cancers and 15,945 controls in Sweden. Molecular subtype was predicted from pathology record-derived IHC markers by a classifier trained on PAM50 subtyping. Multinomial logistic regression estimated separate ORs for each subtype by the exposures parity, age at first birth, breastfeeding, menarche, hormone replacement therapy use, somatotype at age 18, benign breast disease, mammographic density, polygenic risk score, family history of breast cancer, and BRCA mutations. We found clear subtype heterogeneity for genetic factors and breastfeeding. Polygenic risk score was associated with all subtypes except for the basal-like (Pheterogeneity < 0.0001). "Never breastfeeding" was associated with increased risk of basal-like subtype [OR 4.17; 95% confidence interval (CI) 1.89-9.21] compared with both nulliparity (reference) and breastfeeding. Breastfeeding was not associated with risk of HER2-overexpressing type, but protective for all other subtypes. The observed heterogeneity in risk of distinct breast cancer subtypes for germline variants supports heterogeneity in etiology and has implications for their use in risk prediction. The association between basal-like subtype and breastfeeding merits more research into potential causal mechanisms and confounders. Cancer Res; 77(13); 3708-17. ©2017 AACR.
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Affiliation(s)
- Johanna Holm
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.
| | - Louise Eriksson
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Oncology and Pathology, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Alexander Ploner
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Mattias Rantalainen
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Per Hall
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden
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Strand F, Humphreys K, Cheddad A, Törnberg S, Azavedo E, Shepherd J, Hall P, Czene K. Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study. Breast Cancer Res 2016; 18:100. [PMID: 27716311 PMCID: PMC5053212 DOI: 10.1186/s13058-016-0761-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 09/21/2016] [Indexed: 11/10/2022] Open
Abstract
Background Interval breast cancers are often diagnosed at a more advanced stage than screen-detected cancers. Our aim was to identify features in screening mammograms of the normal breast that would differentiate between future interval cancers and screen-detected cancers, and to understand how each feature affects tumor detectability. Methods From a population-based cohort of invasive breast cancer cases in Stockholm-Gotland, Sweden, diagnosed from 2001 to 2008, we analyzed the contralateral mammogram at the preceding negative screening of 394 interval cancer cases and 1009 screen-detected cancers. We examined 32 different image features in digitized film mammograms, based on three alternative dense area identification methods, by a set of logistic regression models adjusted for percent density with interval cancer versus screen-detected cancer as the outcome. Features were forward-selected into a multiple logistic regression model adjusted for mammographic percent density, age, BMI and use of hormone replacement therapy. The associations of the identified features were assessed also in a sample from an independent cohort. Results Two image features, ‘skewness of the intensity gradient’ and ‘eccentricity’, were associated with the risk of interval compared with screen-detected cancer. For the first feature, the per-standard deviation odds ratios were 1.32 (95 % CI: 1.12 to 1.56) and 1.21 (95 % CI: 1.04 to 1.41) in the primary and validation cohort respectively. For the second feature, they were 1.20 (95 % CI: 1.04 to 1.39) and 1.17 (95%CI: 0.98 to 1.39) respectively. The first feature was associated with the tumor size at screen detection, while the second feature was associated with the tumor size at interval detection. Conclusions We identified two novel mammographic features in screening mammograms of the normal breast that differentiated between future interval cancers and screen-detected cancers. We present a starting point for further research into features beyond percent density that might be relevant for interval cancer, and suggest ways to use this information to improve screening. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0761-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fredrik Strand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden. .,Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden.,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Solna, Sweden
| | - Abbas Cheddad
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden
| | - Sven Törnberg
- Department of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden
| | - Edward Azavedo
- Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden
| | - John Shepherd
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, Stockholm, SE-171 77, Sweden
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Busana MC, Eng A, Denholm R, Dowsett M, Vinnicombe S, Allen S, Dos-Santos-Silva I. Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study. Breast Cancer Res 2016; 18:96. [PMID: 27670914 PMCID: PMC5037867 DOI: 10.1186/s13058-016-0756-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 09/08/2016] [Indexed: 11/22/2022] Open
Abstract
Background Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known. Methods We compared the performance of the semi-automated Cumulus method on digital raw, “analogue-like” raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk. Results Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and “analogue-like” raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64). Conclusions The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0756-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marta Cecilia Busana
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Amanda Eng
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.,Centre for Public Health Research, Massey University, Wellington, New Zealand
| | - Rachel Denholm
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Mitch Dowsett
- Academic Biochemistry, Royal Marsden Hospital, London, UK
| | - Sarah Vinnicombe
- Cancer Research, Ninewells Hospital Medical School, University of Dundee, Dundee, UK
| | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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Strand F, Humphreys K, Eriksson M, Li J, Andersson TML, Törnberg S, Azavedo E, Shepherd J, Hall P, Czene K. Longitudinal fluctuation in mammographic percent density differentiates between interval and screen-detected breast cancer. Int J Cancer 2016; 140:34-40. [PMID: 27615710 DOI: 10.1002/ijc.30427] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 08/24/2016] [Indexed: 11/07/2022]
Abstract
Interval breast cancer (IC) has a more aggressive phenotype and higher mortality than screen-detected cancer (SDC). In this case-case study, we investigated whether the size of longitudinal fluctuations in mammographic percent density (PD fluctuation) was associated with the ratio of IC versus SDC among screened women with breast cancer. The primary study population consisted of 1,414 postmenopausal breast cancer cases, and the validation population of 1,241 cases. We calculated PD fluctuation as the quadratic mean of deviations between actual PD and the long-term trend estimated by a mixed effects model. In a logistic regression model we examined the association between PD fluctuation and IC versus SDC including adjustments for PD at last screening, age at diagnosis, BMI and hormone replacement therapy. All statistical tests were two-sided. There were 385 IC and 1,029 SDC in the primary study population, with PD fluctuations of 0.44 and 0.41 respectively (p = 0.0309). After adjustments, PD fluctuation was associated with an increased ratio of IC versus SDC, with an estimated per-standard deviation odds ratio of 1.17 (95% CI = 1.03-1.33), compared to 1.19 (95% CI = 1.04-1.38) in the validation population. In screened women with breast cancer, high fluctuation in mammographic percent density was associated with an increased ratio of IC versus SDC. Whether this is entirely related to a reduced mammographic detectability or to a biological phenotype promoting faster tumor growth remains to be elucidated.
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Affiliation(s)
- Fredrik Strand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Genome Institute of Singapore, Singapore, Singapore
| | - Therese M L Andersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sven Törnberg
- Department of Cancer Screening, Stockholm-Gotland Regional Cancer Centre, Stockholm, Sweden
| | - Edward Azavedo
- Department of Diagnostic Radiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - John Shepherd
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California, San Francisco, CA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS. Radiology 2016; 282:348-355. [PMID: 27598536 DOI: 10.1148/radiol.2016152062] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. © RSNA, 2016.
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Affiliation(s)
- Abra M Jeffers
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Weiva Sieh
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Jafi A Lipson
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Joseph H Rothstein
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Valerie McGuire
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Alice S Whittemore
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
| | - Daniel L Rubin
- From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305
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Li J, Ivansson E, Klevebring D, Tobin NP, Lindström LS, Holm J, Prochazka G, Cristando C, Palmgren J, Törnberg S, Humphreys K, Hartman J, Frisell J, Rantalainen M, Lindberg J, Hall P, Bergh J, Grönberg H, Czene K. Molecular Differences between Screen-Detected and Interval Breast Cancers Are Largely Explained by PAM50 Subtypes. Clin Cancer Res 2016; 23:2584-2592. [DOI: 10.1158/1078-0432.ccr-16-0967] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 08/08/2016] [Accepted: 08/15/2016] [Indexed: 11/16/2022]
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Lau S, Ng KH, Abdul Aziz YF. Volumetric breast density measurement: sensitivity analysis of a relative physics approach. Br J Radiol 2016; 89:20160258. [PMID: 27452264 DOI: 10.1259/bjr.20160258] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE To investigate the sensitivity and robustness of a volumetric breast density (VBD) measurement system to errors in the imaging physics parameters including compressed breast thickness (CBT), tube voltage (kVp), filter thickness, tube current-exposure time product (mAs), detector gain, detector offset and image noise. METHODS 3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images. RESULTS Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p < 0.0001). Maximum shifts in the mean (1.2%) and median (1.1%) VBD of the study population occurred when CBT was varied. CONCLUSION Volpara was robust to expected clinical variations, with errors in most investigated parameters giving limited changes in results, although extreme variations in CBT and kVp could lead to greater errors. ADVANCES IN KNOWLEDGE Despite Volpara's robustness, rigorous quality control is essential to keep the parameter errors within reasonable bounds. Volpara appears robust within those bounds, albeit for more advanced applications such as tracking density change over time, it remains to be seen how accurate the measures need to be.
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Affiliation(s)
- Susie Lau
- 1 Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,2 University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kwan Hoong Ng
- 1 Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,2 University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Yang Faridah Abdul Aziz
- 1 Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,2 University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Throckmorton AD, Rhodes DJ, Hughes KS, Degnim AC, Dickson-Witmer D. Dense Breasts: What Do Our Patients Need to Be Told and Why? Ann Surg Oncol 2016; 23:3119-27. [PMID: 27401446 DOI: 10.1245/s10434-016-5400-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Indexed: 11/18/2022]
Abstract
More than 50 % of states have state-mandated density notification for patients with heterogeneously or extremely dense breasts. Increased breast density carries a risk of masking a cancer and delaying diagnosis. Supplemental imaging is optional and often recommended for certain patients. There are no evidence-based consensus guidelines for screening patients with density as their only risk factor. Breast cancer risk assessment and breast cancer prevention strategies should be discussed with women with dense breasts.
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Affiliation(s)
- Alyssa D Throckmorton
- Department of Surgery, Vanderbilt University, Nashville, TN, USA. .,Baptist Cancer Center, Memphis, TN, USA.
| | | | - Kevin S Hughes
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Amy C Degnim
- Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Diana Dickson-Witmer
- Helen F. Graham Cancer Center and Research Institute, Christiana Care Health System, Newark, DE, USA
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Tan M, Zheng B, Leader JK, Gur D. Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1719-28. [PMID: 26886970 PMCID: PMC4938728 DOI: 10.1109/tmi.2016.2527619] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
The purpose of this study is to develop and test a new computerized model for predicting near-term breast cancer risk based on quantitative assessment of bilateral mammographic image feature variations in a series of negative full-field digital mammography (FFDM) images. The retrospective dataset included series of four sequential FFDM examinations of 335 women. The last examination in each series ("current") and the three most recent "prior" examinations were obtained. All "prior" examinations were interpreted as negative during the original clinical image reading, while in the "current" examinations 159 cancers were detected and pathologically verified and 176 cases remained cancer-free. From each image, we initially computed 158 mammographic density, structural similarity, and texture based image features. The absolute subtraction value between the left and right breasts was selected to represent each feature. We then built three support vector machine (SVM) based risk models, which were trained and tested using a leave-one-case-out based cross-validation method. The actual features used in each SVM model were selected using a nested stepwise regression analysis method. The computed areas under receiver operating characteristic curves monotonically increased from 0.666±0.029 to 0.730±0.027 as the time-lag between the "prior" (3 to 1) and "current" examinations decreases. The maximum adjusted odds ratios were 5.63, 7.43, and 11.1 for the three "prior" (3 to 1) sets of examinations, respectively. This study demonstrated a positive association between the risk scores generated by a bilateral mammographic feature difference based risk model and an increasing trend of the near-term risk for having mammography-detected breast cancer.
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Affiliation(s)
- Maxine Tan
- School of Electrical and Computer Engineering, University of
Oklahoma, Norman, OK 73019 USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of
Oklahoma, Norman, OK 73019 USA
| | - Joseph K. Leader
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA
15213 USA
| | - David Gur
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA
15213 USA
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Chen JH, Lee YW, Chan SW, Yeh DC, Chang RF. Breast Density Analysis with Automated Whole-Breast Ultrasound: Comparison with 3-D Magnetic Resonance Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:1211-1220. [PMID: 26831342 DOI: 10.1016/j.ultrasmedbio.2015.12.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 10/28/2015] [Accepted: 12/16/2015] [Indexed: 06/05/2023]
Abstract
In this study, a semi-automatic breast segmentation method was proposed on the basis of the rib shadow to extract breast regions from 3-D automated whole-breast ultrasound (ABUS) images. The density results were correlated with breast density values acquired with 3-D magnetic resonance imaging (MRI). MRI images of 46 breasts were collected from 23 women without a history of breast disease. Each subject also underwent ABUS. We used Otsu's thresholding method on ABUS images to obtain local rib shadow information, which was combined with the global rib shadow information (extracted from all slice projections) and integrated with the anatomy's breast tissue structure to determine the chest wall line. The fuzzy C-means classifier was used to extract the fibroglandular tissues from the acquired images. Whole-breast volume (WBV) and breast percentage density (BPD) were calculated in both modalities. Linear regression was used to compute the correlation of density results between the two modalities. The consistency of density measurement was also analyzed on the basis of intra- and inter-operator variation. There was a high correlation of density results between MRI and ABUS (R(2) = 0.798 for WBV, R(2) = 0.825 for PBD). The mean WBV from ABUS images was slightly smaller than the mean WBV from MR images (MRI: 342.24 ± 128.08 cm(3), ABUS: 325.47 ± 136.16 cm(3), p < 0.05). In addition, the BPD calculated from MR images was smaller than the BPD from ABUS images (MRI: 24.71 ± 15.16%, ABUS: 28.90 ± 17.73%, p < 0.05). The intra-operator and inter-operator variant analysis results indicated that there was no statistically significant difference in breast density measurement variation between the two modalities. Our results revealed a high correlation in WBV and BPD between MRI and ABUS. Our study suggests that ABUS provides breast density information useful in the assessment of breast health.
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Affiliation(s)
- Jeon-Hor Chen
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Yan-Wei Lee
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Si-Wa Chan
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Dah-Cherng Yeh
- Breast Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Kallenberg M, Petersen K, Nielsen M, Ng AY, Igel C, Vachon CM, Holland K, Winkel RR, Karssemeijer N, Lillholm M. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1322-1331. [PMID: 26915120 DOI: 10.1109/tmi.2016.2532122] [Citation(s) in RCA: 278] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.
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Zheng Y, Keller BM, Ray S, Wang Y, Conant EF, Gee JC, Kontos D. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. Med Phys 2016; 42:4149-60. [PMID: 26133615 DOI: 10.1118/1.4921996] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
PURPOSE Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. METHODS Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. RESULTS The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). CONCLUSIONS The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.
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Affiliation(s)
- Yuanjie Zheng
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Brad M Keller
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Shonket Ray
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Yan Wang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, Pennsylvania 19104
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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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Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches. Cancer Causes Control 2016; 27:481-91. [PMID: 26847236 DOI: 10.1007/s10552-016-0722-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 01/16/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Mammographic density (MD) varies throughout a woman's life. We compared the performance of a fully automated (ImageJ-based) method to the observer-dependent Cumulus approach in the assessment of within-woman changes in MD over time. METHODS MD was assessed in annual pre-diagnostic films (from age 40 to early 50s) from 313 breast cancer cases and 452 matched controls using Cumulus (left medio-lateral oblique (MLO) readings) and the ImageJ-based method (mean left-right MLO readings). Linear mixed models were used to compare within-woman changes in MD among controls. Associations between individual-specific MD trajectories and breast cancer were examined using conditional logistic regression. RESULTS The age-related trajectories predicted by Cumulus and the ImageJ-based method were similar for all MD measures, except that the ImageJ-based method yielded slightly higher (by 2.54%, 95% CI 2.07%, 3.00%) estimates for percent MD. For both methods, the yearly rate of change in percent MD was twice faster after menopause than before, and higher BMI was associated with lower mean percent MD, but not associated with rate of change. Both methods yielded similar associations of individual-specific MD trajectories with breast cancer risk. CONCLUSIONS The ImageJ-based method is a valid fully automated alternative to Cumulus for measuring within-woman changes in MD in digitized films. The Age Trial is registered as an International Standard Randomized Controlled Trial, number ISRCTN24647151.
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Holm J, Li J, Darabi H, Eklund M, Eriksson M, Humphreys K, Hall P, Czene K. Associations of Breast Cancer Risk Prediction Tools With Tumor Characteristics and Metastasis. J Clin Oncol 2016; 34:251-8. [DOI: 10.1200/jco.2015.63.0624] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Purpose The association between established risk factors for breast cancer and subtypes or prognosis of the disease is not well known. We analyzed whether the Tyrer-Cuzick–predicted 10-year breast cancer risk score (TCRS), mammographic density (MD), and a 77-single nucleotide polymorphism polygenic risk score (PRS) were associated with breast cancer tumor prognosticators and risk of distant metastasis. Patients and Methods We used a case-only design in a population-based cohort of 5,500 Swedish patients with breast cancer. Logistic and multinomial logistic regression of outcomes, estrogen receptor (ER) status, lymph node involvement, tumor size, and grade was performed with TCRS, PRS, and percent MD as exposures. Cox proportional hazard models were used to estimate hazard ratios (HRs) of distant metastasis. Results Women at high risk for breast cancer based on PRS and/or TCRS were significantly more likely to be diagnosed with favorable prognosticators, such as ER-positive and low-grade tumors. In contrast, PRS weighted on ER-negative disease was associated with ER-negative tumors. When stratifying by age, the associations of TCRS with favorable prognosticators were restricted to women younger than age 50. Women scoring high in both TCRS and PRS had a lower risk of distant metastasis (HR, 0.69; 95% CI, 0.49 to 0.98). MD was not associated with any of the examined prognosticators. Conclusion Women at high risk for breast cancer based on genetic and lifestyle factors were significantly more likely to be diagnosed with breast cancers with a favorable prognosis. Better knowledge of subtype-specific risk factors could be vital for the success of prevention programs aimed at lowering mortality.
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Affiliation(s)
- Johanna Holm
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Jingmei Li
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Hatef Darabi
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- All authors: Karolinska Institutet, Stockholm, Sweden
| | | | | | - Per Hall
- All authors: Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- All authors: Karolinska Institutet, Stockholm, Sweden
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