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Bitencourt AGV. The impact of AI implementation in mammographic screening: redefining dense breast screening practices. Eur Radiol 2024; 34:6296-6297. [PMID: 38662101 DOI: 10.1007/s00330-024-10761-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 03/15/2024] [Accepted: 03/23/2024] [Indexed: 04/26/2024]
Affiliation(s)
- Almir G V Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, São Paulo, Brazil.
- DASA, São Paulo, Brazil.
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2
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Zaki-Metias KM, Wang H, Tawil TF, Miles EB, Deptula L, Agrawal P, Davis KM, Spalluto LB, Seely JM, Yong-Hing CJ. Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks? Can Assoc Radiol J 2024; 75:593-600. [PMID: 38420877 DOI: 10.1177/08465371241234544] [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: 03/02/2024] Open
Abstract
Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.
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Affiliation(s)
- Kaitlin M Zaki-Metias
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Huijuan Wang
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Tima F Tawil
- Department of Radiology, Trinity Health Oakland Hospital/Wayne State University School of Medicine, Pontiac, MI, USA
| | - Eda B Miles
- Department of Internal Medicine, Arnot Ogden Medical Center, Elmira, NY, USA
| | - Lisa Deptula
- Ross University School of Medicine, Bridgetown, Barbados
| | - Pooja Agrawal
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
- Department of Internal Medicine, HCA Houston Healthcare Kingwood, Houston, TX, USA
| | - Katie M Davis
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lucy B Spalluto
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Nashville, TN, USA
- Veterans Health Administration, Tennessee Valley Healthcare System Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, USA
| | - Jean M Seely
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
| | - Charlotte J Yong-Hing
- Diagnostic Imaging, BC Cancer Vancouver, Vancouver, BC, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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3
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Khosravi P. Editorial for "Automated Breast Density Assessment in MRI Using Deep Learning and Radiomics: Strategies for Reducing Inter-Observer Variability". J Magn Reson Imaging 2024; 60:92-93. [PMID: 37818764 DOI: 10.1002/jmri.29059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 10/13/2023] Open
Affiliation(s)
- Pegah Khosravi
- Department of Biological Sciences, New York City College of Technology, CUNY, New York City, New York, USA
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4
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Kai C, Morita T, Sato I, Yoshida A, Kodama N, Kasai S. The Usefulness of the Breast Density Assessment Application Used by Breast Radiologists. Cureus 2024; 16:e62560. [PMID: 39027798 PMCID: PMC11254854 DOI: 10.7759/cureus.62560] [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] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A Breast Density Assessment application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.
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Affiliation(s)
- Chiharu Kai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JPN
- Department of Health and Welfare, Graduate School, Niigata University of Health and Welfare, Niigata, JPN
| | - Takako Morita
- Department of Breast Surgery, National Hospital Organization, Nagoya Medical Center, Aichi, JPN
| | - Ikumi Sato
- Department of Health and Welfare, Graduate School, Niigata University of Health and Welfare, Niigata, JPN
- Department of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata, JPN
| | - Akifumi Yoshida
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JPN
| | - Naoki Kodama
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JPN
| | - Satoshi Kasai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, JPN
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5
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Mann RM. Breast Screening with US Transmission Imaging: A New Approach Yielding Old Results. Radiology 2024; 311:e241074. [PMID: 38888483 DOI: 10.1148/radiol.241074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Affiliation(s)
- Ritse M Mann
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, the Netherlands; and Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
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6
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Mizzi D, Allely CS, Zarb F, Mercer CE. Implementing supplementary breast cancer screening in women with dense breasts: Insights from European radiographers and radiologists. Radiography (Lond) 2024; 30:908-919. [PMID: 38615593 DOI: 10.1016/j.radi.2024.04.003] [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: 02/06/2024] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION In response to the critical need for enhancing breast cancer screening for women with dense breasts, this study explored the understanding of challenges and requirements for implementing supplementary breast cancer screening for such women among clinical radiographers and radiologists in Europe. METHOD Fourteen (14) semi-structured online interviews were conducted with European clinical radiologists (n = 5) and radiographers (n = 9) specializing in breast cancer screening from 8 different countries: Denmark, Finland, Greece, Italy, Malta, the Netherlands, Switzerland, United Kingdom. The interview schedule comprised questions regarding professional background and demographics and 13 key questions divided into six subgroups, namely Supplementary Imaging, Training, Resources and Guidelines, Challenges, Implementing supplementary screening and Women's Perspective. Data analysis followed the six phases of reflexive thematic analysis. RESULTS Six significant themes emerged from the data analysis: Understanding and experiences of supplementary imaging for women with dense breasts; Challenges and requirements related to training among clinical radiographers and radiologists; Awareness among radiographers and radiologists of guidelines on imaging women with dense breasts; Challenges to implement supplementary screening; Predictors of Implementing Supplementary screening; Views of radiologists and radiographers on women's perception towards supplementary screening. CONCLUSION The interviews with radiographers and radiologists provided valuable insights into the challenges and potential strategies for implementing supplementary breast cancer screening. These challenges included patient and staff related challenges. Implementing multifaceted solutions such as Artificial Intelligence integration, specialized training and resource investment can address these challenges and promote the successful implementation of supplementary screening. Further research and collaboration are needed to refine and implement these strategies effectively. IMPLICATIONS FOR PRACTICE This study highlights the urgent need for specialized training programs and dedicated resources to enhance supplementary breast cancer screening for women with dense breasts in Europe. These resources include advanced imaging technologies, such as MRI or ultrasound, and specialized software for image analysis. Moreover, further research is imperative to refine screening protocols and evaluate their efficacy and cost-effectiveness, based on the findings of this study.
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Affiliation(s)
- D Mizzi
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, MSD 2080, Malta.
| | - C S Allely
- School of Health and Society, University of Salford, Manchester, M5 4WT, United Kingdom.
| | - F Zarb
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, MSD 2080, Malta.
| | - C E Mercer
- School of Health and Society, University of Salford, Manchester, M5 4WT, United Kingdom.
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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Kai C, Otsuka T, Nara M, Kondo S, Futamura H, Kodama N, Kasai S. Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence. Front Oncol 2024; 14:1255109. [PMID: 38505584 PMCID: PMC10949406 DOI: 10.3389/fonc.2024.1255109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 02/12/2024] [Indexed: 03/21/2024] Open
Abstract
Background Mammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT). Methods We used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles. Results The average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40-49, 50-59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40-49, 50-59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40-59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36. Conclusion The study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40-59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.
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Affiliation(s)
- Chiharu Kai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, Japan
- Major in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Niigata, Japan
| | | | - Miyako Nara
- Department of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Disease Center, Komagome Hospital, Tokyo, Japan
| | - Satoshi Kondo
- Graduate School of Engineering, Muroran Institute of Technology, Muroran, Hokkaido, Japan
| | - Hitoshi Futamura
- Healthcare Business Headquarters, Konica Minolta, Inc., Tokyo, Japan
| | - Naoki Kodama
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, Japan
| | - Satoshi Kasai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, Japan
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Saleh GA, Batouty NM, Gamal A, Elnakib A, Hamdy O, Sharafeldeen A, Mahmoud A, Ghazal M, Yousaf J, Alhalabi M, AbouEleneen A, Tolba AE, Elmougy S, Contractor S, El-Baz A. Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review. Cancers (Basel) 2023; 15:5216. [PMID: 37958390 PMCID: PMC10650187 DOI: 10.3390/cancers15215216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
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Affiliation(s)
- Gehad A. Saleh
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Nihal M. Batouty
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt; (G.A.S.)
| | - Abdelrahman Gamal
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elnakib
- Electrical and Computer Engineering Department, School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA;
| | - Omar Hamdy
- Surgical Oncology Department, Oncology Centre, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Marah Alhalabi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.)
| | - Amal AbouEleneen
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Ahmed Elsaid Tolba
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
- The Higher Institute of Engineering and Automotive Technology and Energy, New Heliopolis, Cairo 11829, Egypt
| | - Samir Elmougy
- Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt (A.E.T.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
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Gegios AR, Peterson MS, Fowler AM. Breast Cancer Screening and Diagnosis: Recent Advances in Imaging and Current Limitations. PET Clin 2023; 18:459-471. [PMID: 37296043 DOI: 10.1016/j.cpet.2023.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Breast cancer detection has a significant impact on population health. Although there are many breast imaging modalities, mammography is the predominant tool for breast cancer screening. The introduction of digital breast tomosynthesis to mammography has contributed to increased cancer detection rates and decreased recall rates. In average-risk women, starting annual screening mammography at age 40 years has demonstrated the highest mortality reduction. In intermediate- and high-risk women as well as in those with dense breasts, additional modalities, including MRI, ultrasound, and molecular breast imaging, can also be considered for adjunct screening to improve the detection of mammographically occult malignancy.
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Affiliation(s)
- Alison R Gegios
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Molly S Peterson
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Amy M Fowler
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
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11
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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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12
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Monticciolo DL, Newell MS, Moy L, Lee CS, Destounis SV. Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR. J Am Coll Radiol 2023; 20:902-914. [PMID: 37150275 DOI: 10.1016/j.jacr.2023.04.002] [Citation(s) in RCA: 67] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/26/2023] [Accepted: 04/06/2023] [Indexed: 05/09/2023]
Abstract
Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.
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Affiliation(s)
- Debra L Monticciolo
- Division Chief, Breast Imaging, Massachusetts General Hospital, Boston, Massachusetts.
| | - Mary S Newell
- Interim Division Chief, Breast Imaging, Emory University, Atlanta, Georgia
| | - Linda Moy
- Associate Chair for Faculty Mentoring, New York University Grossman School of Medicine, New York, New York; Editor-in-Chief, Radiology
| | - Cindy S Lee
- New York University Grossman School of Medicine, New York, New York
| | - Stamatia V Destounis
- Elizabeth Wende Breast Care, Rochester, New York; Chair, ACR Commission on Breast Imaging
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Watanabe AT, Retson T, Wang J, Mantey R, Chim C, Karimabadi H. Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability. Diagnostics (Basel) 2023; 13:2694. [PMID: 37627953 PMCID: PMC10453732 DOI: 10.3390/diagnostics13162694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/27/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student's t-test were used to assess the model and reader performance. The model achieved a κ of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a κ of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (κ improved from 0.70 to 0.88, p ≤ 0.001) and intra-reader variability (κ improved from 0.83 to 0.95, p ≤ 0.01) for four-class density, and significant reduction in inter-reader variability (κ improved from 0.77 to 0.96, p ≤ 0.001) and intra-reader variability (κ improved from 0.89 to 0.97, p ≤ 0.01) for binary non-dense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions.
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Affiliation(s)
- Alyssa T. Watanabe
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA
- CureMetrix, Inc., San Diego, CA 92101, USA (R.M.); (H.K.)
| | - Tara Retson
- Department of Radiology, University of California, San Diego, CA 92093, USA
| | - Junhao Wang
- CureMetrix, Inc., San Diego, CA 92101, USA (R.M.); (H.K.)
| | - Richard Mantey
- CureMetrix, Inc., San Diego, CA 92101, USA (R.M.); (H.K.)
| | - Chiyung Chim
- CureMetrix, Inc., San Diego, CA 92101, USA (R.M.); (H.K.)
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Kai C, Ishizuka S, Otsuka T, Nara M, Kondo S, Futamura H, Kodama N, Kasai S. Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence. Cancers (Basel) 2023; 15:2794. [PMID: 37345132 DOI: 10.3390/cancers15102794] [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: 04/08/2023] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023] Open
Abstract
Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.
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Affiliation(s)
- Chiharu Kai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata City 950-3198, Niigata, Japan
| | - Sachi Ishizuka
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata City 950-3198, Niigata, Japan
| | | | - Miyako Nara
- Department of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Disease Center, Komagome Hospital, Tokyo 113-8677, Japan
| | - Satoshi Kondo
- Graduate School of Engineering, Muroran Institute of Technology, Muroran City 050-8585, Hokkaido, Japan
| | | | - Naoki Kodama
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata City 950-3198, Niigata, Japan
| | - Satoshi Kasai
- Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata City 950-3198, Niigata, Japan
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Jiang G, He Z, Zhou Y, Wei J, Xu Y, Zeng H, Wu J, Qin G, Chen W, Lu Y. Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity. Med Phys 2023; 50:837-853. [PMID: 36196045 DOI: 10.1002/mp.16007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 06/25/2022] [Accepted: 07/23/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. METHOD To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. RESULTS 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. CONCLUSIONS The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity.
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Affiliation(s)
- Gongfa Jiang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Yuanpin Zhou
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China
| | - Jun Wei
- Perception Vision Medical Technology Company Ltd., Guangzhou, P. R. China.,Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuesheng Xu
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
| | - Hui Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Jiefang Wu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Genggeng Qin
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, P. R. China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China.,Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, P. R. China
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16
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Klock J. Clinical Importance of 3D Volography in Breast Imaging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1403:239-249. [PMID: 37495921 DOI: 10.1007/978-3-031-21987-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
The clinical applications of the volography algorithm and concomitant refraction-corrected reflection algorithm as described in Chap. 10 are discussed here. Comparisons with an H&E stained image, discussion of glandular tissue visibility, related biomarkers, segmentation accuracy and capabilities, microcalcification and cyst detection and analysis, and various VGA and clinical studies show the unique capabilities of the method. The accuracy of the fibroglandular segmentation and its relevance to breast density in imaging is mentioned. The compatibility with artificial intelligence (AI) is shown and clinical results discussed, concluding that low-frequency 3D ultrasound volography is a powerful 3D ultrasound imaging technique for microanatomic and quantitative features of the breast with good potential for AI utilization to provide an imaging technique that will quantitatively improve clinical performance.
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Maimone S, Morozov AP, Letter HP, Robinson KA, Wasserman MC, Li Z, Maxwell RW. Abbreviated Molecular Breast Imaging: Feasibility and Future Considerations. JOURNAL OF BREAST IMAGING 2022; 4:590-599. [PMID: 38416994 DOI: 10.1093/jbi/wbac060] [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: 05/20/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE Molecular breast imaging (MBI) is a supplemental screening modality consistently demonstrating incremental cancer detection over mammography alone; however, its lengthy duration may limit widespread utilization. The study purpose was to assess feasibility of an abbreviated MBI protocol, providing readers with mediolateral oblique (MLO) projections only and assessing performance in lesion detection and localization. METHODS Retrospective IRB-exempt blinded reader study administered to 5 fellowship-trained breast imaging radiologists. Independent reads performed for 124 screening MBI cases, half abnormal and half negative/normal. Readers determined whether an abnormality was present, side of abnormality, and location of abnormality (medial/lateral). Abnormal cases had confirmatory biopsy or surgical pathology; normal cases had imaging follow-up ensuring true negative results. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess performance. A false negative result indicated that a reader failed to detect abnormal uptake; a false positive result indicated a reader incorrectly called an abnormality for a negative case. Tests for association included chi-square, Fisher-exact, and analysis of variance. RESULTS Mean reader performance for detecting abnormal uptake: sensitivity 96.8%, specificity 98.7%, PPV 98.8%, and NPV 96.9%. Accuracy in localizing lesions to the medial or lateral breast was 100%. There were no associations in reader performance with reader experience, reader technique, lesion morphology, or lesion pathology. Median lesion size was 1.0 cm (range: 0.4-8.0 cm). All readers correctly identified 97.7% (42/43) of lesions with malignant or elevated risk pathology. CONCLUSION An abbreviated MBI protocol (MLO images only) maintained high accuracy in lesion detection and localization.
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Affiliation(s)
- Santo Maimone
- Mayo Clinic Florida, Department of Radiology, Jacksonville, FL, USA
| | - Andrey P Morozov
- Mayo Clinic Florida, Department of Radiology, Jacksonville, FL, USA
| | - Haley P Letter
- Mayo Clinic Florida, Department of Radiology, Jacksonville, FL, USA
| | | | | | - Zhuo Li
- Mayo Clinic Florida, Department of Biostatistics, Jacksonville, FL, USA
| | - Robert W Maxwell
- Mayo Clinic Florida, Department of Radiology, Jacksonville, FL, USA
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18
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Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types. Cancers (Basel) 2022; 14:cancers14205003. [PMID: 36291787 PMCID: PMC9599904 DOI: 10.3390/cancers14205003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/10/2022] [Indexed: 11/24/2022] Open
Abstract
Simple Summary The DL model predictions in automated breast density assessment were independent of the imaging technologies, moderately or substantially agreed with the clinical reader density values, and had improved performance as compared to inclusion of commercial software values. Abstract Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss’ Kappa score between-observers ranged from 0.31–0.50 and 0.55–0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61–0.66 and 0.70–0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.
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19
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Gastounioti A, Eriksson M, Cohen EA, Mankowski W, Pantalone L, Ehsan S, McCarthy AM, Kontos D, Hall P, Conant EF. External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women. Cancers (Basel) 2022; 14:cancers14194803. [PMID: 36230723 PMCID: PMC9564051 DOI: 10.3390/cancers14194803] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64−0.72) for all women, 0.67 (0.61−0.72) for White women, and 0.70 (0.65−0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.
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Affiliation(s)
- Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Correspondence: (A.G.); (E.F.C.); Tel.: +1-314-286-0553 (A.G.); +1-2156624032 (E.F.C.)
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Eric A. Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Walter Mankowski
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lauren Pantalone
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Oncology, Södersjukhuset, 118 83 Stockholm, Sweden
| | - Emily F. Conant
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence: (A.G.); (E.F.C.); Tel.: +1-314-286-0553 (A.G.); +1-2156624032 (E.F.C.)
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20
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Elezaby MA. Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice: A Methodologic Framework for Clinical Testing of Artificial Intelligence Tools. J Am Coll Radiol 2022; 19:1031-1033. [PMID: 35690078 DOI: 10.1016/j.jacr.2022.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 10/18/2022]
Affiliation(s)
- Mai A Elezaby
- Associate Section Chief, Breast Imaging and Intervention Section, Associate Program Director, Breast Imaging Fellowship, and Associate Program Director, Diagnostic Radiology Residency, Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
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21
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Apprehending the Effect of Internet of Things (IoT) Enables Big Data Processing through Multinetwork in Supporting High-Quality Food Products to Reduce Breast Cancer. J FOOD QUALITY 2022. [DOI: 10.1155/2022/2275517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Medical science in recent times has witnessed the large implications of AI-based IoT approaches that made the clinical process easier than before. However, effective IoT technologies can connect as well as exchange necessary clinical data with other healthcare systems and devices conducted across the vast Internet facilities. With the help of IoT-enabled big data processing technologies, physicians can measure accurate weight, blood pressure, and daily symptoms related to spreading breast cancer cases across the globe. Utilizing IoT is essential for providing proper medical assistance, treatment, and detection at the initial stages within the healthcare environment regulated by the facilities of the Internet of Things. The implementation of IoT-based big data processes food products for supporting the detection and prevention of breast cancer. The study supports in making a critical analysis on the role of IoT in the big data mainly in cancer detection and increasing the quality of food products. The study’s main scope is to employ IoT-enabled big data processing to aid in the identification of breast cancer. However, the research is mainly focused on studying the assistance offered to healthcare professionals and others in identifying the disease effectively. The overall research study is going to investigate the role of IoT in the early detection of breast cancer symptoms. A total of 20 women were studied and certain factors have been identified which are the early symptoms of breast cancer and can potentially cause breast cancer. These include age, family history, breast density, and breast temperature (independent variables). A dependent variable has been selected: probability of breast cancer occurrence. After that, linear regression analysis has been carried out to understand how the independent variables impact the dependent variable. Findings showed that age, family history of cancer, breast density, and breast temperature are some measurable factors for breast cancer detection. The work contributes to a critical investigation of the function of IoT in big data, specifically in cancer detection and improving food product quality. Age acceleration increases the risk of breast cancer development; breast temperature increases slightly during cancer formation, and breast density has a positive impact on cancer development. Lastly, this study has provided a future scope of using IoT in cancer detection and prevention.
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22
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Chikarmane S. Synthetic Mammography: Review of Benefits and Drawbacks in Clinical Use. JOURNAL OF BREAST IMAGING 2022; 4:124-134. [PMID: 38417004 DOI: 10.1093/jbi/wbac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Indexed: 03/01/2024]
Abstract
Digital breast tomosynthesis (DBT) has been widely adopted as a breast cancer screening tool, demonstrating decreased recall rates and other improved screening performance metrics when compared to digital mammography (DM) alone. Drawbacks of DBT when added to 2D DM include the increased radiation dose and longer examination time. Synthetic mammography (SM), a 2D reconstruction from the tomosynthesis slices, has been introduced to eliminate the need for a separate acquisition of 2D DM. Data show that the replacement of 2D DM by SM, when used with DBT, maintains the benefits of DBT, such as decreased recall rates, improved cancer detection rates, and similar positive predictive values. Key differences between SM and 2D DM include how the image is acquired, assessment of breast density, and visualization of mammographic findings, such as calcifications. Although SM is approved by the Food and Drug Administration and has been shown to be non-inferior when used with DBT, concerns surrounding SM include image quality and artifacts. The purpose of this review article is to review the benefits, drawbacks, and screening performance metrics of SM versus DBT.
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Affiliation(s)
- Sona Chikarmane
- Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA
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23
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Wimmer M, Sluiter G, Major D, Lenis D, Berg A, Neubauer T, Buhler K. Multi-Task Fusion for Improving Mammography Screening Data Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:937-950. [PMID: 34788218 DOI: 10.1109/tmi.2021.3129068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.
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Destounis S, Arieno A, Santacroce A. Comparison of Cancers Detected by Screening Breast Ultrasound and Digital Breast Tomosynthesis. Acad Radiol 2022; 29:339-347. [PMID: 33589308 DOI: 10.1016/j.acra.2021.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE Review of outcomes of screening patients imaged with both digital breast tomosynthesis (DBT) and screening ultrasound (US) to compare the cancer detection rates and characteristics of cancers detected by the imaging modalities. MATERIAL AND METHODS This retrospective study reviewed a total of 24,787 screening US exams performed in the time period of January 2013 through December 2017. These exams were in patients with heterogeneously dense or extremely dense breast tissue. In this population, 21,220 (86%) had DBT screening mammography. These cases were further reviewed to identify any pathology-proven malignancy detected with US and/or DBT. RESULTS The study cohort consisted of 115 breast cancers in patients having screening US and DBT. Of the 115 cancers, 100 were invasive cancers and 15 were ductal carcinoma in situ: 64/115 were seen on DBT, 9 of which were seen only on DBT, and 106 were seen on US, with 51 seen only on US. The cancer detection rate of DBT only was 0.4/1000 (9/21,220) and 3.0/1000 (64/21,220) for those detected on DBT whether with or without additional US, with detection on US only having an incremental cancer detection rate of 2.4/1000 (51/21,220) above DBT detected malignancies. Differences in DBT-detected lesions and US only lesions when comparing median lesion size, lesion type, tumor type (invasive vs noninvasive) and tumor stage were statistically significant (p = 0.0045, p = 0.0113, p = 0.0003, and p = 0.0153, respectively). CONCLUSION In review of the outcomes of a screening US program, we found a similar number of breast cancers were detected by DBT and US, and US alone (47.8% vs 44.3%, respectively). Ninety-six percent of the cancers detected by US alone were invasive; 89% of those seen on both modalities were invasive, while most of the breast cancers seen on DBT only were in situ carcinoma. Statistically significant differences between DBT and US, and US alone were found for many lesion characteristics including lesion size, type, tumor size, and tumor stage.
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Affiliation(s)
- Stamatia Destounis
- Elizabeth Wende Breast Care, LLC., 170 Sawgrass Drive, Rochester, NY 14620.
| | - Andrea Arieno
- Elizabeth Wende Breast Care, LLC., 170 Sawgrass Drive, Rochester, NY 14620
| | - Amanda Santacroce
- Elizabeth Wende Breast Care, LLC., 170 Sawgrass Drive, Rochester, NY 14620
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Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res 2022; 24:14. [PMID: 35184757 PMCID: PMC8859891 DOI: 10.1186/s13058-022-01509-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
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Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Shyam Desai
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vinayak S Ahluwalia
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Shen C, Klein RW, Moss JL, Dodge DG, Chetlen AL, Stahl KA, Zhou S, Leslie DL, Ruffin MT, Lengerich EJ. Association Between Dense Breast Legislation and Cancer Stage at Diagnosis. Am J Prev Med 2021; 61:890-899. [PMID: 34376293 DOI: 10.1016/j.amepre.2021.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Many states have mandated breast density notification and insurance coverage for additional screening; yet, the association between such legislation and stage of diagnosis for breast cancer is unclear. This study investigates this association and examines the differential impacts among different age and race/ethnicity subgroups. METHODS The Surveillance, Epidemiology, and End Results database was queried to identify patients with breast cancer aged 40-74 years diagnosed between 2005 and 2016. Using a difference-in-differences multinomial logistic model, the odds of being diagnosed at different stages of cancer relative to the localized stage depending on legislation and individual characteristics were examined. Analyses were conducted in 2020-2021. RESULTS The study included 689,641 cases. Overall, the impact of notification legislation was not significant, whereas insurance coverage legislation was associated with 6% lower odds (OR=0.94, 95% CI=0.91, 0.96) of being diagnosed at the regional stage. The association between insurance coverage legislation and stage of diagnosis was even stronger among women aged 40-49 years, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.96) of being diagnosed at the regional stage and 12% lower odds (OR=0.88, 95% CI=0.81, 0.96) of being diagnosed at the distant stage. Hispanic women benefited from notification laws, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.97) of being diagnosed at distant stage. Neither notification nor supplemental screening insurance coverage legislation showed a substantial impact on Black women. CONCLUSIONS The findings imply that improving insurance coverage is more important than being notified overall. Raising awareness is important among Hispanic women; improving communication about dense breasts and access to screening might be more important than legislation among Black women.
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Affiliation(s)
- Chan Shen
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania.
| | - Roger W Klein
- Department of Economics, Rutgers University, New Brunswick, New Jersey
| | - Jennifer L Moss
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania; Department of Family and Community Medicine, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Daleela G Dodge
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania
| | - Alison L Chetlen
- Penn State Cancer Institute, Hershey, Pennsylvania; Department of Radiology, Milton S. Hershey Medical Center, Penn State Health, Hershey, Pennsylvania
| | - Kelly A Stahl
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Shouhao Zhou
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Douglas L Leslie
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Mack T Ruffin
- Penn State Cancer Institute, Hershey, Pennsylvania; Department of Family and Community Medicine, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Eugene J Lengerich
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania
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Gilbert FJ, Hickman SE, Baxter GC, Allajbeu I, James J, Caraco C, Vinnicombe S. Opportunities in cancer imaging: risk-adapted breast imaging in screening. Clin Radiol 2021; 76:763-773. [PMID: 33820637 DOI: 10.1016/j.crad.2021.02.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.
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Affiliation(s)
- F J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S E Hickman
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - G C Baxter
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - I Allajbeu
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - J James
- Nottingham Breast Institute, City Hospital, Nottingham, UK
| | - C Caraco
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - S Vinnicombe
- Thirlestaine Breast Centre, Cheltenham, UK; Ninewells Hospital and Medical School, University of Dundee, UK
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Jiang G, Wei J, Xu Y, He Z, Zeng H, Wu J, Qin G, Chen W, Lu Y. Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2080-2091. [PMID: 33826513 DOI: 10.1109/tmi.2021.3071544] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM using current DBT/FFDM combo images. Gradient guided conditional generative adversarial networks (GGGAN) objective function was designed to preserve subtle MCs and the perceptual loss was exploited to improve the performance of the proposed DCNN on perceptual quality. We used various image quality criteria for evaluation, including preserving masses and MCs which are important in mammogram. Experiment results demonstrated progressive performance improvement of network using different objective functions in terms of those image quality criteria. The methodology we exploited in the SDM generation task to analyze and progressively improve image quality by designing objective functions may be helpful to other image generation tasks.
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Quantitative Breast Density in Contrast-Enhanced Mammography. J Clin Med 2021; 10:jcm10153309. [PMID: 34362092 PMCID: PMC8348046 DOI: 10.3390/jcm10153309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 11/16/2022] Open
Abstract
Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.
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Pawar SD, Sharma KK, Sapate SG, Yadav GY. Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.08.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Dietzel M, Clauser P, Kapetas P, Schulz-Wendtland R, Baltzer PAT. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. ROFO-FORTSCHR RONTG 2021; 193:898-908. [PMID: 33535260 DOI: 10.1055/a-1346-0095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. METHODS AND RESULTS This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. CONCLUSION Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. KEY POINTS · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.. CITATION FORMAT · Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.
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Affiliation(s)
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
| | | | - Pascal Andreas Thomas Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Gender Imaging, Medical University Vienna, Vienna, Austria
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Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol 2020; 216:860-873. [PMID: 33295802 DOI: 10.2214/ajr.20.24894] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BI-RADS is a communication and data tracking system that has evolved since its inception as a brief mammography lexicon and reporting guide into a robust structured reporting platform and comprehensive quality assurance tool for mammography, ultrasound, and MRI. Consistent and appropriate use of the BI-RADS lexicon terminology and assessment categories effectively communicates findings, estimates the risk of malignancy, and provides management recommendations to patients and referring clinicians. The impact of BI-RADS currently extends internationally through six language translations. A condensed version has been proposed to facilitate a phased implementation of BI-RADS in resource-constrained regions. The primary advance of the 5th edition of BI-RADS is harmonization of the lexicon terms across mammography, ultrasound, and MRI. Harmonization has also been achieved across these modalities for the reporting structure, assessment categories, management recommendations, and data tracking system. Areas for improvement relate to certain common findings that lack lexicon descriptors and a need for further clarification of proper use of category 3. BI-RADS is anticipated to continue to evolve for application to a range of emerging breast imaging modalities.
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Barba D, León-Sosa A, Lugo P, Suquillo D, Torres F, Surre F, Trojman L, Caicedo A. Breast cancer, screening and diagnostic tools: All you need to know. Crit Rev Oncol Hematol 2020; 157:103174. [PMID: 33249359 DOI: 10.1016/j.critrevonc.2020.103174] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 09/18/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.
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Affiliation(s)
- Diego Barba
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Ariana León-Sosa
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Paulina Lugo
- Hospital de los Valles HDLV, Quito, Ecuador; Fundación Ayuda Familiar y Comunitaria AFAC, Quito, Ecuador
| | - Daniela Suquillo
- Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Ingeniería en Procesos Biotecnológicos, Colegio de Ciencias Biológicas y Ambientales COCIBA, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Fernando Torres
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Hospital de los Valles HDLV, Quito, Ecuador
| | - Frederic Surre
- University of Glasgow, James Watt School of Engineering, Glasgow, G12 8QQ, United Kingdom
| | - Lionel Trojman
- LISITE, Isep, 75006, Paris, France; Universidad San Francisco de Quito USFQ, Colegio de Ciencias e Ingenierías Politécnico - USFQ, Instituto de Micro y Nanoelectrónica, IMNE, USFQ, Quito, Ecuador
| | - Andrés Caicedo
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Sistemas Médicos SIME, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
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Wang L, Strigel RM. Supplemental Screening for Patients at Intermediate and High Risk for Breast Cancer. Radiol Clin North Am 2020; 59:67-83. [PMID: 33223001 DOI: 10.1016/j.rcl.2020.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The sensitivity of mammography is more limited in patients with dense breasts and some patients at higher risk for breast cancer. Patients with intermediate or high risk for breast cancer may begin screening earlier and benefit from supplemental screening techniques beyond standard 2-dimensional mammography. A patient's individual risk factors for developing breast cancer, their breast density, and the evidence supporting specific modalities for a given clinical scenario help to determine the need for supplemental screening and the modality chosen. Additional factors include the availability of supplemental screening techniques at an individual institution, cost, insurance coverage, and state-specific breast density legislation.
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Affiliation(s)
- Lilian Wang
- Northwestern Medicine, Chicago, IL, USA; Prentice Women's Hospital, 250 East Superior Street, 4th Floor, Room 04-2304, Chicago, IL 60611, USA
| | - Roberta M Strigel
- Breast Imaging and Intervention, University of Wisconsin, 600 Highland Avenue, Madison, WI 53792-3252, USA.
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Rahmat K, Ab Mumin N, Ramli Hamid MT, Fadzli F, Ng WL, Muhammad Gowdh NF. Evaluation of automated volumetric breast density software in comparison with visual assessments in an Asian population: A retrospective observational study. Medicine (Baltimore) 2020; 99:e22405. [PMID: 32991467 PMCID: PMC7523847 DOI: 10.1097/md.0000000000022405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
This study aims to compare Quantra, as an automated volumetric breast density (Vbd) tool, with visual assessment according to ACR BI-RADS density categories and to determine its potential usage in clinical practice.Five hundred randomly selected screening and diagnostic mammograms were included in this retrospective study. Three radiologists independently assigned qualitative ACR BI-RADS density categories to the mammograms. Quantra automatically calculates the volumetric density data into the system. The readers were blinded to the Quantra and other readers assessment. Inter-reader agreement and agreement between Quantra and each reader were tested. Region under the curve (ROC) analysis was performed to obtain the cut-off value to separate dense from a non-dense breast. Results with P value <.05 was taken as significant.There were 40.4% Chinese, 27% Malays, 19% Indian and 3.6% represent other ethnicities. The mean age of the patients was 57. 15%, 45.6%, 30.4%, and 9% of patients fall under BI-RADS A, B, C and D density category respectively. Fair agreement with Kappa (κ) value: 0.49, 0.38, and 0.30 were seen for Reader 1, 2 and 3 versus Quantra. Moderate agreement with κ value: 0.63, 0.64, 0.51 was seen when the data were dichotomized (density A and B to "non-dense", C and D to "dense"). The cut-off Vbd value was 13.5% to stratify dense from non-dense breasts with a sensitivity of 86.2% and specificity of 83.1% (AUC 91.4%; confidence interval: 88.8, 94.1).Quantra showed moderate agreement with radiologists visual assessment. Hence, this study adds to the available evidence to support the potential use of Quantra as an adjunct tool for breast density assessment in routine clinical practice in the Asian population. We found 13.5% is the best cut-off value to stratify dense to non-dense breasts in our study population. Its application will provide an objective, consistent and reproducible results as well as aiding clinical decision-making on the need for supplementary breast ultrasound in our screening population.
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Affiliation(s)
- Kartini Rahmat
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur
| | - Nazimah Ab Mumin
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Marlina Tanty Ramli Hamid
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Farhana Fadzli
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur
| | - Wei Lin Ng
- Department of Biomedical Imaging, University of Malaya Research Imaging Centre, Kuala Lumpur
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Jarosik P, Klimonda Z, Lewandowski M, Byra M. Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.04.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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