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Hernández-Vázquez MA, Hernández-Rodríguez YM, Cortes-Rojas FD, Bayareh-Mancilla R, Cigarroa-Mayorga OE. Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture. Diagnostics (Basel) 2024; 14:1691. [PMID: 39125567 PMCID: PMC11311263 DOI: 10.3390/diagnostics14151691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.
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Affiliation(s)
- Miguel Alejandro Hernández-Vázquez
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2580, Ciudad de México 07340, Mexico (Y.M.H.-R.)
| | - Yazmín Mariela Hernández-Rodríguez
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2580, Ciudad de México 07340, Mexico (Y.M.H.-R.)
| | - Fausto David Cortes-Rojas
- Departamento de Ingeniería Eléctrica/Sección de Bioelectrónica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, Ciudad de México 07360, Mexico;
| | - Rafael Bayareh-Mancilla
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2580, Ciudad de México 07340, Mexico (Y.M.H.-R.)
| | - Oscar Eduardo Cigarroa-Mayorga
- Departamento de Tecnologías Avanzadas, UPIITA-Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2580, Ciudad de México 07340, Mexico (Y.M.H.-R.)
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Kim E, Lewin AA. Breast Density: Where Are We Now? Radiol Clin North Am 2024; 62:593-605. [PMID: 38777536 DOI: 10.1016/j.rcl.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.
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Affiliation(s)
- Eric Kim
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Alana A Lewin
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA; New York University Grossman School of Medicine, New York University Langone Health, Laura and Isaac Perlmutter Cancer Center, 160 East 34th Street 3rd Floor, New York, NY 10016, USA.
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Brown AL, Vijapura C, Patel M, De La Cruz A, Wahab R. Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities. Radiographics 2023; 43:e230024. [PMID: 37792590 DOI: 10.1148/rg.230024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.
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Affiliation(s)
- Ann L Brown
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Charmi Vijapura
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Mitva Patel
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Alexis De La Cruz
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Rifat Wahab
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
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Berg WA, Bandos AI, Sava MG. Analytic Hierarchy Process Analysis of Patient Preferences for Contrast-Enhanced Mammography Versus MRI as Supplemental Screening Options for Breast Cancer. J Am Coll Radiol 2023; 20:758-768. [PMID: 37394083 DOI: 10.1016/j.jacr.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/20/2023] [Accepted: 05/03/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE To guide implementation of supplemental breast screening by assessing patient preferences for contrast-enhanced mammography (CEM) versus MRI using analytic hierarchy process (AHP) methodology. METHODS In an institutional review board-approved, HIPAA-compliant protocol, from March 23 to June 3, 2022, we contacted 579 women who had both CEM screening and MRI. Women were e-mailed an invitation to complete an online survey developed using an AHP-based model to elicit preferences for CEM or MRI. Methods for categorical data analysis were used to evaluate factors affecting preferences, under the Bonferroni correction for multiplicity. RESULTS Complete responses were received from 222 (38.3%) women; the 189 women with a personal history of breast cancer had a mean age 61.8 years, and the 34 women without a personal history of breast cancer had a mean age of 53.6 years. Of 222 respondents, 157 (70.7%, confidence interval [CI]: 64.7-76.7) were determined to prefer CEM to MRI. Breast positioning was the most important criterion for 74 of 222 (33.3%) respondents, with claustrophobia, intravenous line placement, and overall stress most important for 38, 37, and 39 women (17.1%, 16.7%, and 17.6%), respectively, and noise level, contrast injection, and indifference being emphasized least frequently (by 10 [4.5%], 11 [5.0%], and 13 [5.9%] women, respectively). CEM preference was most prevalent (MRI least prevalent) for respondents emphasizing claustrophobia (37 of 38 [97%], CI: 86.2-99.9); CEM preference was least prevalent (MRI most prevalent) for respondents emphasizing breast positioning (40 of 74 [54%], CI: 42.1-65.7). CONCLUSIONS AHP-based modeling reveals strong patient preferences for CEM over MRI, with claustrophobia favoring preference for CEM and breast positioning relatively favoring preference for MRI. Our results should help guide implementation of screening CEM and MRI.
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Affiliation(s)
- Wendie A Berg
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, Pennsylvania; ACR and the Society of Breast Imaging, Honorary Fellow of the Austrian Roentgen Society, and voluntary Chief Scientific Advisor to DenseBreast-info website.
| | - Andriy I Bandos
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
| | - M Gabriela Sava
- Wilbur O. and Ann Powers College of Business, Clemson University, Clemson, South Carolina; current affiliation: Department of Applied Statistics and Operations Research, Allen W. and Carol M. Schmidhorst College of Business, Bowling Green State University, Bowling Green, Ohio
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Portnow LH, Choridah L, Kardinah K, Handarini T, Pijnappel R, Bluekens AMJ, Duijm LEM, Schoub PK, Smilg PS, Malek L, Leung JWT, Raza S. International Interobserver Variability of Breast Density Assessment. J Am Coll Radiol 2023; 20:671-684. [PMID: 37127220 DOI: 10.1016/j.jacr.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/22/2023] [Accepted: 03/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States. METHODS Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression. RESULTS For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories. CONCLUSIONS Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.
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Affiliation(s)
- Leah H Portnow
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; and Instructor, Department of Radiology, Harvard Medical School, Boston, Massachusetts.
| | - Lina Choridah
- Vice Dean of Research and Development, Department of Radiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jalan Farmako, Sekip Utara, Yogyakarta, Indonesia
| | - Kardinah Kardinah
- Director of Early Breast Cancer Detection Program for the Ministry of Health and Medical Committee Leader of Quality Assurance; Department of Radiology, Faculty of Medicine, Dharmais Cancer Hospital/National Cancer Center, Jakarta, Indonesia
| | - Triwulan Handarini
- Chair of the Radiology Medical Staff, Department of Radiology, Faculty of Medicine, Airlangga University-Dr Soetomo Academic General Hospital, Surabaya, Indonesia
| | - Ruud Pijnappel
- Department of Radiology, University Medical Center, Utrecht, the Netherlands; Professor, Utrecht University, Utrecht, the Netherlands; Chair, Dutch Expert Centre for Screening; and President, European Society of Breast Imaging
| | - Adriana M J Bluekens
- Department of Radiology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, the Netherlands
| | - Lucien E M Duijm
- Department of Radiology, Canisius-Wilhelmina Ziekenhuis, Nijmegen, the Netherlands
| | - Peter K Schoub
- Department of Radiology, Parklane Radiology, Johannesburg, South Africa; Chair, Breast Imaging Society of South Africa
| | - Pamela S Smilg
- Department of Radiology, Parklane Radiology, Johannesburg, South Africa; Department of Radiology, Donald Gordon Medical Centre, Johannesburg, South Africa
| | - Liat Malek
- The Breast Wellness Centre, Johannesburg, South Africa
| | - Jessica W T Leung
- Deputy Chair, Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas; and Chair, Ultrasound Subcommittee, BI-RADS Committee, American College of Radiology. https://twitter.com/DrJessicaLeung
| | - Sughra Raza
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Dartmouth Hitchcock Medical Center, Hanover, NH; and Editor-in-Chief, Journal of Global Radiology
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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.
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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
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Harvey JA. Breast Density and Breast Cancer Risk. JOURNAL OF BREAST IMAGING 2022; 4:339-341. [PMID: 38416985 DOI: 10.1093/jbi/wbac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Indexed: 03/01/2024]
Affiliation(s)
- Jennifer A Harvey
- University of Rochester Medical Center, Department of Imaging Sciences, Rochester, NY, USA
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