1
|
Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Hopper JL, Li S. Mammographic Texture versus Conventional Cumulus Measure of Density in Breast Cancer Risk Prediction: A Literature Review. Cancer Epidemiol Biomarkers Prev 2024; 33:989-998. [PMID: 38787323 DOI: 10.1158/1055-9965.epi-23-1365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/01/2024] [Accepted: 05/22/2024] [Indexed: 05/25/2024] Open
Abstract
Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field.
Collapse
Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Genetic Technologies Limited, Fitzroy, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| |
Collapse
|
2
|
Yaghjyan L, Wang Z, Warner ET, Rosner B, Heine J, Tamimi RM. Reproductive Factors Related to Childbearing and a Novel Automated Mammographic Measure, V. Cancer Epidemiol Biomarkers Prev 2024; 33:804-811. [PMID: 38497795 PMCID: PMC11147729 DOI: 10.1158/1055-9965.epi-23-1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/06/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND We investigated the associations between several reproductive factors related to childbearing and the variation (V) measure (a novel, objective, single summary measure of breast image intensity) by menopausal status. METHODS Our study included 3,814 cancer-free women within the Nurses' Health Study (NHS) and NHSII cohorts. The data on reproductive variables and covariates were obtained from biennial questionnaires closest to the mammogram date. V-measures were obtained from mammographic images using a previously developed algorithm capturing the standard deviation of pixel values. We used multivariate linear regression to examine the associations of parity, age at first birth, time between menarche and first birth, time since last pregnancy, and lifetime breastfeeding duration with V-measure, adjusting for breast cancer risk factors, including the percentage of mammographic density (PMD). We further examined whether these associations were statistically accounted for (mediated) by PMD. RESULTS Among premenopausal women, none of the reproductive factors were associated with V. Among postmenopausal women, inverse associations of parity and positive associations of age at first birth with V were mediated by PMD (percent mediated: nulliparity: 66.7%, P < 0.0001; parity: 50.5%, P < 0.01; age at first birth 76.1%, P < 0.001) and were no longer significant in PMD-adjusted models. Lifetime duration of breastfeeding was positively associated with V [>36 vs. 0 ≤1 months β = 0.29; 95% confidence interval (CI) 0.07; 0.52, Ptrend < 0.01], independent of PMD. CONCLUSIONS Parity, age at first birth, and breastfeeding were associated with postmenopausal V. IMPACT This study highlights associations of reproductive factors with mammographic image intensity.
Collapse
Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, University of Florida, College of Public Health and Health Professions and College of Medicine, Gainesville, Florida
| | - Zifan Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Erica T Warner
- Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Bernard Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - John Heine
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| |
Collapse
|
3
|
Behrens A, Wurmthaler L, Heindl F, Gass P, Häberle L, Volz B, Hack CC, Emons J, Erber R, Hartmann A, Beckmann MW, Ruebner M, Dougall WC, Press MF, Fasching PA, Huebner H. RANK and RANKL Expression in Tumors of Patients with Early Breast Cancer. Geburtshilfe Frauenheilkd 2024; 84:77-85. [PMID: 38178900 PMCID: PMC10764119 DOI: 10.1055/a-2192-2998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/15/2023] [Indexed: 01/06/2024] Open
Abstract
Introduction The receptor activator of nuclear factor-κB (RANK) pathway was associated with the pathogenesis of breast cancer. Several studies attempted to link the RANK/RANKL pathway to prognosis; however, with inconsistent outcomes. We aimed to further contribute to the knowledge about RANK/RANKL as prognostic factors in breast cancer. Within this study, protein expression of RANK and its ligand, RANKL, in the tumor tissue was analyzed in association with disease-free survival (DFS) and overall survival (OS) in a study cohort of patients with early breast cancer. Patients and Methods 607 samples of female primary and early breast cancer patients from the Bavarian Breast Cancer Cases and Controls Study were analyzed to correlate the RANK and RANKL expression with DFS and OS. Therefore, expression was quantified using immunohistochemical staining of a tissue microarray. H-scores were determined with the cut-off value of 8.5 for RANK and 0 for RANKL expression, respectively. Results RANK and RANKL immunohistochemistry were assessed by H-score. Both biomarkers did not correlate (ρ = -0.04). According to molecular subtypes, triple-negative tumors and HER2-positive tumors showed a higher number of RANK-positive tumors (H-score ≥ 8.5), however, no subtype-specific expression of RANKL could be detected. Higher RANKL expression tended to correlate with a better prognosis. However, RANK and RANKL expression could not be identified as statistically significant prognostic factors within the study cohort. Conclusions Tumor-specific RANK and RANKL expressions are not applicable as prognostic factors for DFS and OS, but might be associated with subtype-specific breast cancer progression.
Collapse
Affiliation(s)
- Annika Behrens
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Lena Wurmthaler
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Felix Heindl
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Paul Gass
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
- Biostatistics Unit, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bernhard Volz
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Ansbach University of Applied Sciences, Ansbach, Germany
| | - Carolin C. Hack
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Julius Emons
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Ramona Erber
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arndt Hartmann
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias W. Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Matthias Ruebner
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - William C. Dougall
- Hematology and Oncology Research, Amgen, Inc., Seattle, WA, USA
- Immunology in Cancer and Infection Laboratory, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Michael F. Press
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| | - Hanna Huebner
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Bavarian Center for Cancer Research (BZKF), Erlangen, Germany
| |
Collapse
|
4
|
Behrens A, Fasching PA, Schwenke E, Gass P, Häberle L, Heindl F, Heusinger K, Lotz L, Lubrich H, Preuß C, Schneider MO, Schulz-Wendtland R, Stumpfe FM, Uder M, Wunderle M, Zahn AL, Hack CC, Beckmann MW, Emons J. Predicting mammographic density with linear ultrasound transducers. Eur J Med Res 2023; 28:384. [PMID: 37770952 PMCID: PMC10537934 DOI: 10.1186/s40001-023-01327-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/28/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. METHODS We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. RESULTS Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. CONCLUSIONS In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).
Collapse
Affiliation(s)
- Annika Behrens
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany.
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Eva Schwenke
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
- Biostatistics Unit, Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Felix Heindl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Katharina Heusinger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Laura Lotz
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Hannah Lubrich
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Caroline Preuß
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Michael O Schneider
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Department of Radiology, Erlangen University Hospital, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Florian M Stumpfe
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, Erlangen University Hospital, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Anna L Zahn
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| |
Collapse
|
5
|
Edmonds CE, O'Brien SR, Conant EF. Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions. Semin Ultrasound CT MR 2023; 44:35-45. [PMID: 36792272 DOI: 10.1053/j.sult.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.
Collapse
Affiliation(s)
- Christine E Edmonds
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA.
| | - Sophia R O'Brien
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
6
|
Ahn JS, Ebrahimian S, McDermott S, Lee S, Naccarato L, Di Capua JF, Wu MY, Zhang EW, Muse V, Miller B, Sabzalipour F, Bizzo BC, Dreyer KJ, Kaviani P, Digumarthy SR, Kalra MK. Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency. JAMA Netw Open 2022; 5:e2229289. [PMID: 36044215 PMCID: PMC9434361 DOI: 10.1001/jamanetworkopen.2022.29289] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
IMPORTANCE The efficient and accurate interpretation of radiologic images is paramount. OBJECTIVE To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH). MAIN OUTCOMES AND MEASURES The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding. RESULTS A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001). CONCLUSIONS AND RELEVANCE These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.
Collapse
Affiliation(s)
| | - Shadi Ebrahimian
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Internal Medicine, Icahn School of Medicine at Mount Sinai, Elmhurst Hospital Center, Elmhurst, New York
| | - Shaunagh McDermott
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | | | - Laura Naccarato
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - John F. Di Capua
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Markus Y. Wu
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Eric W. Zhang
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Victorine Muse
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Benjamin Miller
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Farid Sabzalipour
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Bernardo C. Bizzo
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Keith J. Dreyer
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| | - Parisa Kaviani
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Subba R. Digumarthy
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mannudeep K. Kalra
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Data Science Office, Mass General Brigham, Boston, Massachusetts
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Hernández A, Miranda DA, Pertuz S. Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106443. [PMID: 34656014 DOI: 10.1016/j.cmpb.2021.106443] [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: 01/13/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today. METHODS We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face. RESULTS Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered. CONCLUSIONS Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.
Collapse
Affiliation(s)
| | | | - Said Pertuz
- Universidad Industrial de Santander, Bucaramanga, Colombia.
| |
Collapse
|
9
|
Gerasimova-Chechkina E, Toner BC, Batchelder KA, White B, Freynd G, Antipev I, Arneodo A, Khalil A. Loss of Mammographic Tissue Homeostasis in Invasive Lobular and Ductal Breast Carcinomas vs. Benign Lesions. Front Physiol 2021; 12:660883. [PMID: 34054577 PMCID: PMC8153084 DOI: 10.3389/fphys.2021.660883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/09/2021] [Indexed: 12/24/2022] Open
Abstract
The 2D wavelet transform modulus maxima (WTMM) method is used to perform a comparison of the spatial fluctuations of mammographic breast tissue from patients with invasive lobular carcinoma, those with invasive ductal carcinoma, and those with benign lesions. We follow a procedure developed and validated in a previous study, in which a sliding window protocol is used to analyze thousands of small subregions in a given mammogram. These subregions are categorized according to their Hurst exponent values (H): fatty tissue (H ≤ 0.45), dense tissue (H ≥ 0.55), and disrupted tissue potentially linked with tumor-associated loss of homeostasis (0.45 < H < 0.55). Following this categorization scheme, we compare the mammographic tissue composition of the breasts. First, we show that cancerous breasts are significantly different than breasts with a benign lesion (p-value ∼ 0.002). Second, the asymmetry between a patient’s cancerous breast and its contralateral counterpart, when compared to the asymmetry from patients with benign lesions, is also statistically significant (p-value ∼ 0.006). And finally, we show that lobular and ductal cancerous breasts show similar levels of disruption and similar levels of asymmetry. This study demonstrates reproducibility of the WTMM sliding-window approach to help detect and characterize tumor-associated breast tissue disruption from standard mammography. It also shows promise to help with the detection lobular lesions that typically go undetected via standard screening mammography at a much higher rate than ductal lesions. Here both types are assessed similarly.
Collapse
Affiliation(s)
| | - Brian C Toner
- CompuMAINE Laboratory, University of Maine, Orono, ME, United States
| | | | - Basel White
- CompuMAINE Laboratory, University of Maine, Orono, ME, United States
| | - Genrietta Freynd
- Department of Pathology, Perm State Medical University Named After Academician E. A. Wagner, Perm, Russia
| | - Igor Antipev
- Department of Pathology, Perm State Medical University Named After Academician E. A. Wagner, Perm, Russia
| | - Alain Arneodo
- Laboratoire Ondes et Matière d'Aquitaine, Universite de Bordeaux, Bordeaux, France
| | - Andre Khalil
- CompuMAINE Laboratory, University of Maine, Orono, ME, United States.,Department of Chemical and Biomedical Engineering, University of Maine, Orono, ME, United States
| |
Collapse
|
10
|
Lee H, Kim C, Bhattacharjee S, Park H, Prakash D, Choi H. A Paradigm Shift in Nuclear Chromatin Interpretation: From Qualitative Intuitive Recognition to Quantitative Texture Analysis of Breast Cancer Cell Nuclei. Cytometry A 2020; 99:698-706. [PMID: 33159476 PMCID: PMC8359278 DOI: 10.1002/cyto.a.24260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 10/02/2020] [Accepted: 11/02/2020] [Indexed: 12/03/2022]
Abstract
Assessing the pattern of nuclear chromatin is essential for pathological investigations. However, the interpretation of nuclear pattern is subjective. In this study, we performed the texture analysis of nuclear chromatin in breast cancer samples to determine the nuclear pleomorphism score thereof. We used three different algorithms for extracting high‐level texture features: the gray‐level co‐occurrence matrix (GLCM), gray‐level run length matrix (GLRLM), and gray‐level size zone matrix (GLSZM). Using these algorithms, 12 GLCM, 11 GLRLM, and 16 GLSZM features were extracted from three scores of breast carcinoma (Scores 1–3). Classification accuracy was assessed using the support vector machine (SVM) and k‐nearest neighbor (KNN) classification models. Three features of GLCM, 11 of GLRLM, and 12 of GLSZM were consistent across the three nuclear pleomorphism scores of breast cancer. Comparing Scores 1 and 3, the GLSZM feature large zone high gray‐level emphasis showed the largest difference among breast cancer nuclear scores among all features of the three algorithms. The SVM and KNN classifiers showed favorable results for all three algorithms. A multiclass classification was performed to compare and distinguish between the scores of breast cancer. Texture features of nuclear chromatin can provide useful information for nuclear scoring. However, further validation of the correlations of histopathologic features, and standardization of the texture analysis process, are required to achieve better classification results. © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.
Collapse
Affiliation(s)
- Hye‐Kyung Lee
- Department of Pathology, College of MedicineEulji UniversityDaejeonKorea
| | - Cho‐Hee Kim
- Department of Digital Anti‐Aging Healthcareu‐AHRC, Inje UniversityGimhaeKorea
| | | | - Hyeon‐Gyun Park
- Department of Computer Engineeringu‐AHRC, Inje UniversityGimhaeKorea
| | | | - Heung‐Kook Choi
- Department of Computer Engineeringu‐AHRC, Inje UniversityGimhaeKorea
| |
Collapse
|
11
|
Vasileiou G, Costa MJ, Long C, Wetzler IR, Hoyer J, Kraus C, Popp B, Emons J, Wunderle M, Wenkel E, Uder M, Beckmann MW, Jud SM, Fasching PA, Cavallaro A, Reis A, Hammon M. Breast MRI texture analysis for prediction of BRCA-associated genetic risk. BMC Med Imaging 2020; 20:86. [PMID: 32727387 PMCID: PMC7388478 DOI: 10.1186/s12880-020-00483-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/10/2020] [Indexed: 01/31/2023] Open
Abstract
Background BRCA1/2 deleterious variants account for most of the hereditary breast and ovarian cancer cases. Prediction models and guidelines for the assessment of genetic risk rely heavily on criteria with high variability such as family cancer history. Here we investigated the efficacy of MRI (magnetic resonance imaging) texture features as a predictor for BRCA mutation status. Methods A total of 41 female breast cancer individuals at high genetic risk, sixteen with a BRCA1/2 pathogenic variant and twenty five controls were included. From each MRI 4225 computer-extracted voxels were analyzed. Non-imaging features including clinical, family cancer history variables and triple negative receptor status (TNBC) were complementarily used. Lasso-principal component regression (L-PCR) analysis was implemented to compare the predictive performance, assessed as area under the curve (AUC), when imaging features were used, and lasso logistic regression or conventional logistic regression for the remaining analyses. Results Lasso-selected imaging principal components showed the highest predictive value (AUC 0.86), surpassing family cancer history. Clinical variables comprising age at disease onset and bilateral breast cancer yielded a relatively poor AUC (~ 0.56). Combination of imaging with the non-imaging variables led to an improvement of predictive performance in all analyses, with TNBC along with the imaging components yielding the highest AUC (0.94). Replacing family history variables with imaging components yielded an improvement of classification performance of ~ 4%, suggesting that imaging compensates the predictive information arising from family cancer structure. Conclusions The L-PCR model uncovered evidence for the utility of MRI texture features in distinguishing between BRCA1/2 positive and negative high-risk breast cancer individuals, which may suggest value to diagnostic routine. Integration of computer-extracted texture analysis from MRI modalities in prediction models and inclusion criteria might play a role in reducing false positives or missed cases especially when established risk variables such as family history are missing.
Collapse
Affiliation(s)
- Georgia Vasileiou
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054, Erlangen, Germany.
| | - Maria J Costa
- Siemens Healthcare, Imaging Analytics Germany, 91054, Erlangen, Germany
| | - Christopher Long
- Siemens Healthcare, Imaging Analytics Germany, 91054, Erlangen, Germany
| | - Iris R Wetzler
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Juliane Hoyer
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054, Erlangen, Germany
| | - Cornelia Kraus
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054, Erlangen, Germany
| | - Bernt Popp
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054, Erlangen, Germany
| | - Julius Emons
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Evelyn Wenkel
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054, Erlangen, Germany
| | - Alexander Cavallaro
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - André Reis
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 10, 91054, Erlangen, Germany
| | - Matthias Hammon
- Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| |
Collapse
|
12
|
Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB, Carlsen JF. Impact of adding breast density to breast cancer risk models: A systematic review. Eur J Radiol 2020; 127:109019. [DOI: 10.1016/j.ejrad.2020.109019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 01/19/2023]
|
13
|
Pertuz S, Sassi A, Arponen O, Holli-Helenius K, Laaperi AL, Rinta-Kiikka I. Do Mammographic Systems Affect the Performance of Computerized Parenchymal Analysis? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4863-4866. [PMID: 31946950 DOI: 10.1109/embc.2019.8856948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Early identification of women at high risk of developing breast cancer is fundamental for timely diagnosis and treatment. Recently, researchers have demonstrated that the computerized analysis of parenchymal (breast tissue) patterns in mammograms can be utilized to assess the risk level of patients. However, parenchymal analysis being an image-based biomarker, its performance may be affected by the acquisition parameters of the mammogram. Unfortunately, research on the effect of the mammographic system on the performance of parenchymal analysis is very scarce. In this paper, we implement a parenchymal analysis algorithm and study the effect of different mammographic systems on its performance. We show in a setting of 286 women that the use of different mammographic systems can yield differences of up to 24% in the area under the ROC curve. Results suggest the the construction of models for risk assessment based on parenchymal analysis should incorporate the imaging technologies into the analysis.
Collapse
|
14
|
Pertuz S, Sassi A, Holli-Helenius K, Kämäräinen J, Rinta-Kiikka I, Lääperi AL, Arponen O. Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample. Eur J Radiol 2019; 121:108710. [PMID: 31689665 DOI: 10.1016/j.ejrad.2019.108710] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 10/04/2019] [Accepted: 10/11/2019] [Indexed: 01/05/2023]
Abstract
PURPOSE To assess the association between breast cancer risk and mammographic parenchymal measures obtained using a fully-automated, publicly available software, OpenBreast. METHODS This retrospective case-control study involved screening mammograms of asymptomatic women diagnosed with breast cancer between 2016 and 2017. The 114 cases were matched with corresponding healthy controls by birth and screening years and the mammographic system used. Parenchymal analysis was performed using OpenBreast, a software implementing a computerized parenchymal analysis algorithm. Breast percent density was measured with an interactive thresholding method. The parenchymal measures were Box-Cox transformed and adjusted for age and percent density. Changes in the odds ratio per standard deviation (OPERA) with 95% confidence intervals (CIs) and the area under the ROC curve (AUC) for parenchymal measures and percent densities were used to evaluate the discrimination between cases and controls. Differences in AUCs were assessed using DeLong's test. RESULTS The adjusted OPERA value of parenchymal measures was 2.49 (95% CI: 1.79-3.47). Parenchymal measures using OpenBreast were more accurate (AUC = 0.779) than percent density (AUC = 0.609) in discriminating between cases and controls (p < 0.001). CONCLUSIONS Parenchymal measures obtained with the evaluated software were positively associated with breast cancer risk and were more accurate than percent density in the prediction of risk.
Collapse
Affiliation(s)
- Said Pertuz
- Connectivity and Signal Processing group, Universidad Industrial de Santander, 680002 Bucaramanga, Colombia; Computing Sciences, Tampere University, 33710 Tampere, Finland.
| | - Antti Sassi
- Department of Radiology, Tampere University Hospital, 33521 Tampere, Finland
| | | | - Joni Kämäräinen
- Computing Sciences, Tampere University, 33710 Tampere, Finland
| | - Irina Rinta-Kiikka
- Department of Radiology, Tampere University Hospital, 33521 Tampere, Finland
| | - Anna-Leena Lääperi
- Department of Radiology, Tampere University Hospital, 33521 Tampere, Finland
| | - Otso Arponen
- Department of Oncology, Tampere University Hospital, 33521 Tampere, Finland
| |
Collapse
|
15
|
Pertuz S, Sassi A, Karivaara-Mäkelä M, Holli-Helenius K, Lääperi AL, Rinta-Kiikka I, Arponen O, Kämäräinen JK. Micro-parenchymal patterns for breast cancer risk assessment. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab42f4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
|
16
|
Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
Collapse
Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| |
Collapse
|
17
|
Lux MP, Emons J, Bani MR, Wunderle M, Sell C, Preuss C, Rauh C, Jud SM, Heindl F, Langemann H, Geyer T, Brandl AL, Hack CC, Adler W, Schulz-Wendtland R, Beckmann MW, Fasching PA, Gass P. Diagnostic Accuracy of Breast Medical Tactile Examiners (MTEs): A Prospective Pilot Study. Breast Care (Basel) 2019; 14:41-47. [PMID: 31019442 DOI: 10.1159/000495883] [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: 11/19/2022] Open
Abstract
Background The usefulness of clinical breast examination (CBE) in general and in breast cancer screening programs has been a matter of debate. This study investigated whether adding vision-impaired medical tactile examiners (MTEs) improves the predictiveness of CBE for suspicious lesions and analyzed the feasibility and acceptability of this approach. Methods The prospective study included 104 patients. Physicians and MTEs performed CBEs, and mammography and ultrasound results were used as the gold standard. Sensitivity and specificity were calculated and logistic regression models were used to compare the predictive value of CBE by physicians alone, MTEs alone, and physicians and MTEs combined. Results For CBEs by physicians alone, MTEs alone, and both combined, sensitivity was 71, 82, and 89% and specificity was 55, 45, and 35%, respectively. Using adjusted logistic regression models, the validated areas under the curve were 0.685, 0.692, and 0.710 (median bootstrapped p value (DeLong) = 0.381). Conclusion The predictive value for a suspicious breast lesion in CBEs performed by MTEs in patients without prior surgery was similar to that of physician-conducted CBEs. Including MTEs in the CBE procedure in breast units thus appears feasible and could be a way of utilizing their skills.
Collapse
Affiliation(s)
- Michael P Lux
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Mayada R Bani
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Charlotte Sell
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Caroline Preuss
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Claudia Rauh
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Felix Heindl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Hanna Langemann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Thomas Geyer
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Anna-Lisa Brandl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Werner Adler
- Institute of Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | | | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen - EMN, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen
| |
Collapse
|
18
|
Tan M, Mariapun S, Yip CH, Ng KH, Teo SH. A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort. Phys Med Biol 2019; 64:035016. [PMID: 30577031 DOI: 10.1088/1361-6560/aafabd] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55-10.64) and for Volpara volumetric breast density, 1.10 (0.67-1.81). The AUC for the LDA trained classifier was 0.68 (0.64-0.73), compared to 0.52 (0.47-0.57) for Volpara volumetric breast density (p < 0.001). The regression analysis of OR values for the LDA classifier also showed a significant increase in slope (p < 0.02). Mammographic texture features derived from digital mammograms are important quantitative measures for breast cancer risk assessment based models. Parenchymal texture analysis has an important role for stratifying breast cancer risk in women, which can be implemented to routine breast cancer screening strategies.
Collapse
Affiliation(s)
- Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, Malaysia. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, United States of America
| | | | | | | | | |
Collapse
|
19
|
|
20
|
Kontos D, Winham SJ, Oustimov A, Pantalone L, Hsieh MK, Gastounioti A, Whaley DH, Hruska CB, Kerlikowske K, Brandt K, Conant EF, Vachon CM. Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment. Radiology 2018; 290:41-49. [PMID: 30375931 DOI: 10.1148/radiol.2018180179] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors. Materials and Methods Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, χ2, and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent case-control sample (76 women diagnosed with breast cancer and 158 control participants) matched on age. Results Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P = .001). Breast density was not strongly correlated with phenotype category (R2 = 0.24 for linear trend). The low- to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P = .001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P = .03 for comparison). Conclusion Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Pinker in this issue.
Collapse
Affiliation(s)
- Despina Kontos
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Stacey J Winham
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Andrew Oustimov
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Lauren Pantalone
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Meng-Kang Hsieh
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Aimilia Gastounioti
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Dana H Whaley
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Carrie B Hruska
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Kathleen Brandt
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Emily F Conant
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| | - Celine M Vachon
- From the Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (D.K., A.O., L.P., M.K.H., A.G., E.F.C.); Department of Health Sciences Research (S.J.W., C.M.V.) and Department of Radiology (D.H.W., C.B.H., K.B.), Mayo Clinic, Rochester, Minn (S.J.W., D.H.W., C.B.H., K.B., C.M.V.); and Departments of Medicine and Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, Calif (K.K.)
| |
Collapse
|
21
|
Gastounioti A, Oustimov A, Hsieh MK, Pantalone L, Conant EF, Kontos D. Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk. Acad Radiol 2018; 25:977-984. [PMID: 29395798 PMCID: PMC6026048 DOI: 10.1016/j.acra.2017.12.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 12/15/2017] [Accepted: 12/22/2017] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES We evaluate utilizing convolutional neural networks (CNNs) to optimally fuse parenchymal complexity measurements generated by texture analysis into discriminative meta-features relevant for breast cancer risk prediction. MATERIALS AND METHODS With Institutional Review Board approval and Health Insurance Portability and Accountability Act compliance, we retrospectively analyzed "For Processing" contralateral digital mammograms (GE Healthcare 2000D/DS) from 106 women with unilateral invasive breast cancer and 318 age-matched controls. We coupled established texture features (histogram, co-occurrence, run-length, structural), extracted using a previously validated lattice-based strategy, with a multichannel CNN into a hybrid framework in which a multitude of texture feature maps are reduced to meta-features predicting the case or control status. We evaluated the framework in a randomized split-sample setting, using the area under the curve (AUC) of the receiver operating characteristic (ROC) to assess case-control discriminatory capacity. We also compared the framework to CNNs directly fed with mammographic images, as well as to conventional texture analysis, where texture feature maps are summarized via simple statistical measures that are then used as inputs to a logistic regression model. RESULTS Strong case-control discriminatory capacity was demonstrated on the basis of the meta-features generated by the hybrid framework (AUC = 0.90), outperforming both CNNs applied directly to raw image data (AUC = 0.63, P <.05) and conventional texture analysis (AUC = 0.79, P <.05). CONCLUSIONS Our results suggest that informative interactions between patterns exist in texture feature maps derived from mammographic images, which can be extracted and summarized via a multichannel CNN architecture toward leveraging the associations of textural measurements to breast cancer risk.
Collapse
Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104.
| | - Andrew Oustimov
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104
| | - Meng-Kang Hsieh
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104
| | - Lauren Pantalone
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3710 Hamilton Walk, Rm D601E Goddard Bldg., Philadelphia, PA 19104
| |
Collapse
|
22
|
Wanders JOP, van Gils CH, Karssemeijer N, Holland K, Kallenberg M, Peeters PHM, Nielsen M, Lillholm M. The combined effect of mammographic texture and density on breast cancer risk: a cohort study. Breast Cancer Res 2018; 20:36. [PMID: 29720220 PMCID: PMC5932877 DOI: 10.1186/s13058-018-0961-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 03/21/2018] [Indexed: 12/21/2022] Open
Abstract
Background Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied. Methods Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50–75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0–6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices. Results The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51–63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16–4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57–0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32–2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51–3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53–0.59) to 0.62 (95% CI 0.58–0.65) (p < 0.001) and from 0.58 (95% CI 0.54–0.61) to 0.60 (95% CI 0.57–0.63) (p = 0.054), respectively. Conclusions Deep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases. Electronic supplementary material The online version of this article (10.1186/s13058-018-0961-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Johanna O P Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands
| | - Carla H van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands.
| | - Nico Karssemeijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Katharina Holland
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Michiel Kallenberg
- Department of Computer Science, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen, Denmark.,Biomediq A/S, Fruebjergvej 3, 2100, Copenhagen, Denmark
| | - Petra H M Peeters
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands.,MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place W2 1PG, London, UK
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen, Denmark.,Biomediq A/S, Fruebjergvej 3, 2100, Copenhagen, Denmark
| | - Martin Lillholm
- Department of Computer Science, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen, Denmark.,Biomediq A/S, Fruebjergvej 3, 2100, Copenhagen, Denmark
| |
Collapse
|
23
|
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.
Collapse
Affiliation(s)
- Yane Li
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
| | | | | | | | | | | |
Collapse
|
24
|
Vinnicombe SJ. Breast density: why all the fuss? Clin Radiol 2017; 73:334-357. [PMID: 29273225 DOI: 10.1016/j.crad.2017.11.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/17/2017] [Indexed: 01/06/2023]
Abstract
The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.
Collapse
Affiliation(s)
- S J Vinnicombe
- Cancer Research, School of Medicine, Level 7, Mailbox 4, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Ali MA, Czene K, Eriksson L, Hall P, Humphreys K. Breast Tissue Organisation and its Association with Breast Cancer Risk. Breast Cancer Res 2017; 19:103. [PMID: 28877713 PMCID: PMC5586066 DOI: 10.1186/s13058-017-0894-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Background Mammographic percentage density is an established and important risk factor for breast cancer. In this paper, we investigate the role of the spatial organisation of (dense vs. fatty) regions of the breast defined from mammographic images in terms of breast cancer risk. Methods We present a novel approach that provides a thorough description of the spatial organisation of different types of tissue in the breast. Each mammogram is first segmented into four regions (fatty, semi-fatty, semi-dense and dense tissue). The spatial relations between each pair of regions is described using so-called forces histograms (FHs) and summarised using functional principal component analysis. In our main analysis, association with case–control status is assessed using a Swedish population-based case–control study (1,170 cases and 1283 controls), for which digitised mammograms were available. We also carried out a small validation study based on digital images. Results For our main analysis, we obtained a global p value of 2×10−7 indicating a significant association between the spatial relations of the four segmented regions and breast cancer status after adjustment for percentage density and other important breast cancer risk factors. Our (spatial relations) score had a per standard deviation odds ratio 1.29, after accounting for overfitting (percentage density had a per standard deviation odds ratio of 1.34). The spatial relations between the fatty and semi-fatty tissue and the spatial relations between the fatty and dense tissue were the most significant. The spatial relations between the fatty and semi-fatty tissue were associated with parity and age at first birth (p=6×10−4). Using digital images, we were able to verify that the same characteristics of tissue organisation can be identified and we validated the association for the spatial relations between the fatty and semi-fatty tissue. Conclusions Our findings are consistent with the notion that fibroglandular and adipose tissue plays a role in breast cancer risk and, more specifically, they suggest that fatty tissue in the lower quadrants and the absence of density in the retromammary space, as shown in mediolateral oblique images, are protective against breast cancer.
Collapse
Affiliation(s)
- Maya Alsheh Ali
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology and Pathology, Cancer Centre Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. .,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
27
|
Häberle L, Hack CC, Heusinger K, Wagner F, Jud SM, Uder M, Beckmann MW, Schulz-Wendtland R, Wittenberg T, Fasching PA. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound. Eur J Med Res 2017; 22:30. [PMID: 28854966 PMCID: PMC5577694 DOI: 10.1186/s40001-017-0270-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 08/11/2017] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure). METHODS This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation. RESULTS About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model. CONCLUSION Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.
Collapse
Affiliation(s)
- Lothar Häberle
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany. .,Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany.
| | - Carolin C Hack
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Katharina Heusinger
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Florian Wagner
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Sebastian M Jud
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Michael Uder
- University Breast Center for Franconia, Institute of Radiology, Comprehensive Cancer Center EMN, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Matthias W Beckmann
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- University Breast Center for Franconia, Institute of Radiology, Comprehensive Cancer Center EMN, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | | | - Peter A Fasching
- University Breast Center for Franconia, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.,Division Hematology/Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| |
Collapse
|
28
|
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.
Collapse
|
29
|
Häberle L, Hein A, Rübner M, Schneider M, Ekici AB, Gass P, Hartmann A, Schulz-Wendtland R, Beckmann MW, Lo WY, Schroth W, Brauch H, Fasching PA, Wunderle M. Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods. Geburtshilfe Frauenheilkd 2017; 77:667-678. [PMID: 28757654 DOI: 10.1055/s-0043-111602] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 05/15/2017] [Accepted: 05/16/2017] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Studies of triple-negative breast cancer have recently been extending the inclusion criteria and incorporating additional molecular markers into the selection criteria, opening up scope for targeted therapies. The screening phases required for studies of this type are often prolonged, since the process of determining the molecular subtype and carrying out additional biomarker assessment is time-consuming. Parameters such as germline genotypes capable of predicting the molecular subtype before it becomes available from pathology might be helpful for treatment planning and optimizing the timing and cost of screening phases. This appears to be feasible, as rapid and low-cost genotyping methods are becoming increasingly available. The aim of this study was to identify single nucleotide polymorphisms (SNPs) for breast cancer risk capable of predicting triple negativity, in addition to clinical predictors, in breast cancer patients. METHODS This cross-sectional observational study included 1271 women with invasive breast cancer who were treated at a university hospital. A total of 76 validated breast cancer risk SNPs were successfully genotyped. Univariate associations between each SNP and triple negativity were explored using logistic regression analyses. Several variable selection and regression techniques were applied to identify a set of SNPs that together improve the prediction of triple negativity in addition to the clinical predictors of age at diagnosis and body mass index (BMI). The most accurate prediction method was determined by cross-validation. RESULTS The SNP rs10069690 (TERT, CLPTM1L) was the only significant SNP (corrected p = 0.02) after correction of p values for multiple testing in the univariate analyses. This SNP and three additional SNPs from the genes RAD51B, CCND1, and FGFR2 were selected for prediction of triple negativity. The addition of these SNPs to clinical predictors increased the cross-validated area under the curve (AUC) from 0.618 to 0.625. Age at diagnosis was the strongest predictor, stronger than any genetic characteristics. CONCLUSION Prediction of triple-negative breast cancer can be improved if SNPs associated with breast cancer risk are added to a prediction rule based on age at diagnosis and BMI. This finding could be used for prescreening purposes in complex molecular therapy studies for triple-negative breast cancer.
Collapse
Affiliation(s)
- Lothar Häberle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Erlangen, Germany
| | - Alexander Hein
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias Rübner
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Schneider
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Institute of Diagnostic Radiology, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Wing-Yee Lo
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tübingen, Tübingen, Germany
| | - Werner Schroth
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tübingen, Tübingen, Germany
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.,University of Tübingen, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| |
Collapse
|
30
|
Müller AV, McEvoy FJ, Tomkiewicz J, Politis SN, Amigo JM. Ultrasonographic predictors of response of European eels (Anguilla anguilla) to hormonal treatment for induction of ovarian development. Am J Vet Res 2017; 77:478-86. [PMID: 27111015 DOI: 10.2460/ajvr.77.5.478] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To examine ultrasonographic predictors of ovarian development in European eels (Anguilla anguilla) undergoing hormonal treatment for assisted reproduction. ANIMALS 83 female European eels. PROCEDURES Eels received weekly IM injections of salmon pituitary extract (first injection = week 1). Ultrasonography of the ovaries was performed twice during hormonal treatment (weeks 7 and 11). Eels were identified on the basis of body weight as having an adequate response by weeks 14 to 20 or an inadequate response after injections for 21 weeks. Eels were euthanized at the end of the experiment and classified by use of ovarian histologic examination. Ovarian cross-sectional area and size of eel (ie, length (3) ) were used to classify eels (fast responder, slow responder, or nonresponder) and to calculate an ultrasonographic-derived gonadosomatic index. Gray-level co-occurrence matrices were calculated from ovarian images, and 22 texture features were calculated from these matrices. RESULTS The ultrasonographic-derived gonadosomatic index differed significantly between fast responders and slow responders or nonresponders at both weeks 7 and 11. Principal component analysis revealed a pattern of separation between the groups, and partial least squares discriminant analysis revealed signals in the ovarian texture that discriminated females that responded to treatment from those that did not. CONCLUSIONS AND CLINICAL RELEVANCE Ovarian texture information in addition to morphometric variables can enhance ultrasonographic applications for assisted reproduction of eels and potentially other fish species. This was a novel, nonlethal method for classifying reproductive response of eels and the first objective texture analysis performed on ultrasonographic images of the gonads of fish.
Collapse
|
31
|
Tan M, Aghaei F, Wang Y, Zheng B. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Phys Med Biol 2016; 62:358-376. [PMID: 27997380 DOI: 10.1088/1361-6560/aa5081] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793 ± 0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
Collapse
Affiliation(s)
- Maxine Tan
- Electrical and Computer Systems Engineering (ECSE) Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, Malaysia. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
| | | | | | | |
Collapse
|
32
|
Malkov S, Shepherd JA, Scott CG, Tamimi RM, Ma L, Bertrand KA, Couch F, Jensen MR, Mahmoudzadeh AP, Fan B, Norman A, Brandt KR, Pankratz VS, Vachon CM, Kerlikowske K. Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Res 2016; 18:122. [PMID: 27923387 PMCID: PMC5139106 DOI: 10.1186/s13058-016-0778-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 11/12/2016] [Indexed: 12/28/2022] Open
Abstract
Background Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density. Methods This study combines five case–control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study. Results Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH10 and FD_TH15) were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH60 to FD_TH85) were associated with a decreased risk. Increasing the FD_TH75 and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with a decreased risk of breast cancer. For example, 1 standard deviation increase of FD_TH75 was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79–0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar. Conclusion Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0778-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Serghei Malkov
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
| | - John A Shepherd
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | | | | | - Lin Ma
- UCSF Departments of Medicine and Epidemiology/Biostatistics, San Francisco, CA, USA
| | | | | | | | - Amir P Mahmoudzadeh
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | - Bo Fan
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA
| | | | | | | | | | - Karla Kerlikowske
- UCSF Departments of Medicine and Epidemiology/Biostatistics, San Francisco, CA, USA
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
Gastounioti A, Conant EF, Kontos D. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res 2016; 18:91. [PMID: 27645219 PMCID: PMC5029019 DOI: 10.1186/s13058-016-0755-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation. MAIN TEXT The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation. CONCLUSIONS The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.
Collapse
Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily F Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
35
|
Gerasimova-Chechkina E, Toner B, Marin Z, Audit B, Roux SG, Argoul F, Khalil A, Gileva O, Naimark O, Arneodo A. Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors. Front Physiol 2016; 7:336. [PMID: 27555823 PMCID: PMC4977307 DOI: 10.3389/fphys.2016.00336] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 07/20/2016] [Indexed: 01/07/2023] Open
Abstract
There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis.
Collapse
Affiliation(s)
| | - Brian Toner
- CompuMAINE Laboratory, Department of Mathematics and Statistics, University of Maine Orono, ME, USA
| | - Zach Marin
- CompuMAINE Laboratory, Department of Mathematics and Statistics, University of Maine Orono, ME, USA
| | - Benjamin Audit
- Université Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Laboratoire de Physique Lyon, France
| | - Stephane G Roux
- Université Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Laboratoire de Physique Lyon, France
| | - Francoise Argoul
- Université Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Laboratoire de PhysiqueLyon, France; Laboratoire Ondes et Matière d'Aquitaine, Centre National de la Recherche Scientifique, Université de Bordeaux, UMR 5798Talence, France
| | - Andre Khalil
- CompuMAINE Laboratory, Department of Mathematics and Statistics, University of Maine Orono, ME, USA
| | - Olga Gileva
- Department of Therapeutic and Propedeutic Dentistry, Perm State Medical University Perm, Russia
| | - Oleg Naimark
- Laboratory of Physical Foundation of Strength, Institute of Continuous Media Mechanics UB RAS Perm, Russia
| | - Alain Arneodo
- Université Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, Laboratoire de PhysiqueLyon, France; Laboratoire Ondes et Matière d'Aquitaine, Centre National de la Recherche Scientifique, Université de Bordeaux, UMR 5798Talence, France
| |
Collapse
|
36
|
Winkel RR, von Euler-Chelpin M, Nielsen M, Petersen K, Lillholm M, Nielsen MB, Lynge E, Uldall WY, Vejborg I. Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study. BMC Cancer 2016; 16:414. [PMID: 27387546 PMCID: PMC4936245 DOI: 10.1186/s12885-016-2450-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 06/21/2016] [Indexed: 01/12/2023] Open
Abstract
Background Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. Methods The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs). Results Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone). Conclusion Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Rikke Rass Winkel
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - My von Euler-Chelpin
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark
| | - Mads Nielsen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.,Biomediq, Fruebjergvej 3, DK-2100, Copenhagen Ø, Denmark
| | - Kersten Petersen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark
| | | | - Michael Bachmann Nielsen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
| | - Elsebeth Lynge
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark
| | - Wei Yao Uldall
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
| | - Ilse Vejborg
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
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.
Collapse
|
39
|
Sun W, Tseng TLB, Qian W, Zhang J, Saltzstein EC, Zheng B, Lure F, Yu H, Zhou S. Using multiscale texture and density features for near-term breast cancer risk analysis. Med Phys 2016; 42:2853-62. [PMID: 26127038 DOI: 10.1118/1.4919772] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. METHODS The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. RESULTS From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). CONCLUSIONS The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.
Collapse
Affiliation(s)
- Wenqing Sun
- College of Engineering, University of Texas at El Paso, El Paso, Texas 79968
| | | | - Wei Qian
- College of Engineering, University of Texas at El Paso, El Paso, Texas 79968 and Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China
| | - Jianying Zhang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China and College of Biological Sciences, University of Texas at El Paso, El Paso, Texas 79968
| | - Edward C Saltzstein
- University Breast Care Center at the Texas Tech University Health Sciences, El Paso, Texas 79905
| | - Bin Zheng
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China and College of Engineering, University of Oklahoma, Norman, Oklahoma 73019
| | - Fleming Lure
- College of Engineering, University of Texas at El Paso, El Paso, Texas 79968 and Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China
| | - Hui Yu
- Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang 550004, China
| | - Shi Zhou
- Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang 550004, China
| |
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Qian W, Sun W, Zheng B. Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches. Expert Rev Med Devices 2015; 12:497-9. [DOI: 10.1586/17434440.2015.1068115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
42
|
Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk. Ann Biomed Eng 2015; 43:2416-28. [PMID: 25851469 DOI: 10.1007/s10439-015-1316-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 03/30/2015] [Indexed: 12/18/2022]
Abstract
The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.
Collapse
|
43
|
Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign. PLoS One 2014; 9:e107580. [PMID: 25222610 PMCID: PMC4164655 DOI: 10.1371/journal.pone.0107580] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 08/20/2014] [Indexed: 12/14/2022] Open
Abstract
The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the “CC-MLO fractal dimension plot”, where a “fractal zone” and “Euclidean zones” (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.
Collapse
|
44
|
Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, Mai PL, Galbo CE, Nichols K, Calzone KA, Olopade OI, Gail MH, Giger ML. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res 2014. [PMID: 25159706 DOI: 10.1186/preaccept-1744229618121391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers. METHODS We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject's digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject's belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model's discriminatory capacity. RESULTS In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density. CONCLUSIONS Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.
Collapse
Affiliation(s)
- Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
45
|
Gierach GL, Li H, Loud JT, Greene MH, Chow CK, Lan L, Prindiville SA, Eng-Wong J, Soballe PW, Giambartolomei C, Mai PL, Galbo CE, Nichols K, Calzone KA, Olopade OI, Gail MH, Giger ML. Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study. Breast Cancer Res 2014; 16:424. [PMID: 25159706 PMCID: PMC4268674 DOI: 10.1186/s13058-014-0424-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 07/31/2014] [Indexed: 12/24/2022] Open
Abstract
Introduction Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers. Methods We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity. Results In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density. Conclusions Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography. Electronic supplementary material The online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
46
|
Fuhrman BJ, Byrne C. Comparing mammographic measures across populations. J Natl Cancer Inst 2014; 106:dju109. [PMID: 24816205 DOI: 10.1093/jnci/dju109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Barbara J Fuhrman
- Affiliations of authors: Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR (BJF); Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD (CB).
| | - Celia Byrne
- Affiliations of authors: Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR (BJF); Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD (CB)
| |
Collapse
|
47
|
Cheddad A, Czene K, Shepherd JA, Li J, Hall P, Humphreys K. Enhancement of mammographic density measures in breast cancer risk prediction. Cancer Epidemiol Biomarkers Prev 2014; 23:1314-23. [PMID: 24722754 DOI: 10.1158/1055-9965.epi-13-1240] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Mammographic density is a strong risk factor for breast cancer. METHODS We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density. RESULTS From analysis of data from a Swedish case-control study (1,286 breast cancer cases and 1,391 control subjects, ages 50-75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75-0.88; P = 6 × 10(-7)] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002). CONCLUSIONS MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment. IMPACT Inclusion of MIP in risk models should be considered for studies using area PD from analog films.
Collapse
Affiliation(s)
- Abbas Cheddad
- Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - John A Shepherd
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California, San Francisco, California; and
| | - Jingmei Li
- Human Genetics, Genome Institute of Singapore, Singapore
| | - Per Hall
- Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;
| |
Collapse
|
48
|
Abstract
Mammography is the central diagnostic method for clinical diagnostics of breast cancer and the breast cancer screening program. In the clinical routine complementary methods, such as ultrasound, tomosynthesis and optional magnetic resonance imaging (MRI) are already combined for the diagnostic procedure. Future developments will utilize investigative procedures either as a hybrid (combination of several different imaging modalities in one instrument) or as a fusion method (the technical fusion of two or more of these methods) to implement fusion imaging into diagnostic algorithms. For screening there are reasonable hypotheses to aim for studies that individualize the diagnostic process within the screening procedure. Individual breast cancer risk prediction and individualized knowledge about sensitivity and specificity for certain diagnostic methods could be tested. The clinical implementation of these algorithms is not yet in sight.
Collapse
|
49
|
Daye D, Keller B, Conant EF, Chen J, Schnall MD, Maidment ADA, Kontos D. Mammographic parenchymal patterns as an imaging marker of endogenous hormonal exposure: a preliminary study in a high-risk population. Acad Radiol 2013; 20:635-46. [PMID: 23570938 DOI: 10.1016/j.acra.2012.12.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Revised: 12/22/2012] [Accepted: 12/23/2012] [Indexed: 11/17/2022]
Abstract
RATIONALE AND OBJECTIVES Parenchymal texture patterns have been previously associated with breast cancer risk, yet their underlying biological determinants remain poorly understood. Here, we investigate the potential of mammographic parenchymal texture as a phenotypic imaging marker of endogenous hormonal exposure. MATERIALS AND METHODS A retrospective cohort study was performed. Digital mammography (DM) images in the craniocaudal (CC) view from 297 women, 154 without breast cancer and 143 with unilateral breast cancer, were analyzed. Menopause status was used as a surrogate of cumulative endogenous hormonal exposure. Parenchymal texture features were extracted and mammographic percent density (MD%) was computed using validated computerized methods. Univariate and multivariable logistic regression analysis was performed to assess the association between texture features and menopause status, after adjusting for MD% and hormonally related confounders. The receiver operating characteristic (ROC) area under the curve (AUC) of each model was estimated to evaluate the degree of association between the extracted mammographic features and menopause status. RESULTS Coarseness, gray-level correlation, and fractal dimension texture features have a significant independent association with menopause status in the cancer-affected population; skewness and fractal dimension exhibit a similar association in the cancer-free population (P < .05). The ROC AUC of the logistic regression model including all texture features was 0.70 (P < .05) for cancer-affected and 0.63 (P < .05) for cancer-free women. Texture features retained significant association with menopause status (P < .05) after adjusting for MD%, age at menarche, ethnicity, contraception use, hormone replacement therapy, parity, and age at first birth. CONCLUSION Mammographic texture patterns may reflect the effect of endogenous hormonal exposure on the breast tissue and may capture such effects beyond mammographic density. Differences in texture features between pre- and postmenopausal women are more pronounced in the cancer-affected population, which may be attributed to an increased association to breast cancer risk. Texture features could ultimately be incorporated in breast cancer risk assessment models as markers of hormonal exposure.
Collapse
Affiliation(s)
- Dania Daye
- Department of Bioengineering, University of Pennsylvania, 3400 Spruce St., Philadelphia, PA 19104, USA.
| | | | | | | | | | | | | |
Collapse
|
50
|
Hack CC, Häberle L, Geisler K, Schulz-Wendtland R, Hartmann A, Fasching PA, Uder M, Wachter DL, Jud SM, Loehberg CR, Lux MP, Rauh C, Beckmann MW, Heusinger K. Mammographic Density and Prediction of Nodal Status in Breast Cancer Patients. Geburtshilfe Frauenheilkd 2013; 73:136-141. [PMID: 24771910 DOI: 10.1055/s-0032-1328291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Revised: 02/14/2013] [Accepted: 02/15/2013] [Indexed: 12/15/2022] Open
Abstract
Aim: Nodal status remains one of the most important prognostic factors in breast cancer. The cellular and molecular reasons for the spread of tumor cells to the lymph nodes are not well understood and there are only few predictors in addition to tumor size and multifocality that give an insight into additional mechanisms of lymphatic spread. Aim of our study was therefore to investigate whether breast characteristics such as mammographic density (MD) add to the predictive value of the presence of lymph node metastases in patients with primary breast cancer. Methods: In this retrospective study we analyzed primary, metastasis-free breast cancer patients from one breast center for whom data on MD and staging information were available. A total of 1831 patients were included into this study. MD was assessed as percentage MD (PMD) using a semiautomated method and two readers for every patient. Multiple logistic regression analyses with nodal status as outcome were used to investigate the predictive value of PMD in addition to age, tumor size, Ki-67, estrogen receptor (ER), progesterone receptor (PR), grading, histology, and multi-focality. Results: Multifocality, tumor size, Ki-67 and grading were relevant predictors for nodal status. Adding PMD to a prediction model which included these factors did not significantly improve the prediction of nodal status (p = 0.24, likelihood ratio test). Conclusion: Nodal status could be predicted quite well with the factors multifocality, tumor size, Ki-67 and grading. PMD does not seem to play a role in the lymphatic spread of tumor cells. It could be concluded that the amount of extracellular matrix and stromal cell content of the breast which is reflected by MD does not influence the probability of malignant breast cells spreading from the primary tumor to the lymph nodes.
Collapse
Affiliation(s)
- C C Hack
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - L Häberle
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - K Geisler
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - R Schulz-Wendtland
- Institut für gynäkologische Radiologie, Universitätsklinikum Erlangen, Erlangen
| | - A Hartmann
- Institute of Pathology, University Hospital Erlangen, Erlangen
| | - P A Fasching
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - M Uder
- Institut für gynäkologische Radiologie, Universitätsklinikum Erlangen, Erlangen
| | - D L Wachter
- Institute of Pathology, University Hospital Erlangen, Erlangen
| | - S M Jud
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - C R Loehberg
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - M P Lux
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - C Rauh
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - M W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| | - K Heusinger
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University , Erlangen-Nuremberg, Erlangen
| |
Collapse
|