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Park EK, Lee H, Kim M, Kim T, Kim J, Kim KH, Kooi T, Chang Y, Ryu S. Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction. Diagnostics (Basel) 2024; 14:1212. [PMID: 38928628 PMCID: PMC11202482 DOI: 10.3390/diagnostics14121212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
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Affiliation(s)
- Eun Kyung Park
- Department of Radiology, We Comfortable Clinic, Seoul 07327, Republic of Korea
| | - Hyeonsoo Lee
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Minjeong Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Taesoo Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Junha Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Ki Hwan Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Thijs Kooi
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Yoosoo Chang
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
| | - Seungho Ryu
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
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Coles CE, Earl H, Anderson BO, Barrios CH, Bienz M, Bliss JM, Cameron DA, Cardoso F, Cui W, Francis PA, Jagsi R, Knaul FM, McIntosh SA, Phillips KA, Radbruch L, Thompson MK, André F, Abraham JE, Bhattacharya IS, Franzoi MA, Drewett L, Fulton A, Kazmi F, Inbah Rajah D, Mutebi M, Ng D, Ng S, Olopade OI, Rosa WE, Rubasingham J, Spence D, Stobart H, Vargas Enciso V, Vaz-Luis I, Villarreal-Garza C. The Lancet Breast Cancer Commission. Lancet 2024; 403:1895-1950. [PMID: 38636533 DOI: 10.1016/s0140-6736(24)00747-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/18/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Affiliation(s)
| | - Helena Earl
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Benjamin O Anderson
- Global Breast Cancer Initiative, World Health Organisation and Departments of Surgery and Global Health Medicine, University of Washington, Seattle, WA, USA
| | - Carlos H Barrios
- Oncology Research Center, Hospital São Lucas, Porto Alegre, Brazil
| | - Maya Bienz
- Mount Vernon Cancer Centre, East and North Hertfordshire NHS Trust, London, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - David A Cameron
- Institute of Genetics and Cancer and Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Fatima Cardoso
- Breast Unit, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Wanda Cui
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Prudence A Francis
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Reshma Jagsi
- Emory University School of Medicine, Atlanta, GA, USA
| | - Felicia Marie Knaul
- Institute for Advanced Study of the Americas, University of Miami, Miami, FL, USA; Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA; Tómatelo a Pecho, Mexico City, Mexico
| | - Stuart A McIntosh
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Kelly-Anne Phillips
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Lukas Radbruch
- Department of Palliative Medicine, University Hospital Bonn, Bonn, Germany
| | | | | | - Jean E Abraham
- Department of Oncology, University of Cambridge, Cambridge, UK
| | | | | | - Lynsey Drewett
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | | | - Farasat Kazmi
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | | | | | - Dianna Ng
- Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Szeyi Ng
- The Institute of Cancer Research, London, UK
| | | | - William E Rosa
- Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | | | | | | | | | | | - Cynthia Villarreal-Garza
- Breast Cancer Center, Hospital Zambrano Hellion TecSalud, Tecnologico de Monterrey, Monterrey, Mexico
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Shamir SB, Sasson AL, Margolies LR, Mendelson DS. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering (Basel) 2024; 11:451. [PMID: 38790318 PMCID: PMC11117903 DOI: 10.3390/bioengineering11050451] [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: 03/21/2024] [Revised: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
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Affiliation(s)
- Stephanie B. Shamir
- Department of Diagnostic, Molecular and Interventional Radiology, The Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Conley CC, Cheraghi N, Anderson A, Rodriguez JD, Ginocchi A, Song JH, Crane E, Mishori R, O'Neill SC. Patterns and Predictors of Referral for Screening Breast MRI: A Mixed-Methods Study. J Womens Health (Larchmt) 2024; 33:639-649. [PMID: 38484303 PMCID: PMC11238842 DOI: 10.1089/jwh.2023.0557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024] Open
Abstract
Introduction: Women with ≥20% lifetime breast cancer risk can receive supplemental breast cancer screening with MRI. We examined factors associated with recommendation for screening breast MRI among primary care providers (PCPs), gynecologists (GYNs), and radiologists. Methods: We conducted a sequential mixed-methods study. Quantitative: Participants (N = 72) reported recommendations for mammogram and breast MRI via clinical vignettes describing hypothetical patients with moderate, high, and very high breast cancer risk. Logistic regressions assessed the relationships of clinician-level factors (gender, specialty, years practicing) and practice-level factors (practice type, imaging facilities available) with screening recommendations. Qualitative: We interviewed a subset of survey participants (n = 17, 17/72 = 24%) regarding their decision-making about breast cancer screening recommendations. Interviews were audio-recorded, transcribed, and analyzed with directed content analysis. Results: Compared with PCPs, GYNs and radiologists were significantly more likely to recommend breast MRI for high-risk (ORs = 4.09 and 4.09, respectively) and very-high-risk patients (ORs = 8.56 and 18.33, respectively). Qualitative analysis identified two key phases along the clinical pathway for high-risk women. Phase 1 was "identifying high-risk women," which included three subthemes (systems for risk assessment, barriers to risk assessment, scope of practice issues). Phase 2 was "referral for screening," which included three subthemes (conflicting guidelines, scope of practice issues, legal implications). Frequency of themes differed between specialties, potentially explaining findings from the quantitative phase. Conclusions: There are significant differences between specialties in supplemental breast cancer screening recommendations. Multilevel interventions are needed to support identification and management of women with high breast cancer risk, particularly for PCPs.
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Affiliation(s)
- Claire C Conley
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
| | - Nora Cheraghi
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
| | - Alaina Anderson
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
| | - Jennifer D Rodriguez
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
| | - Annalisa Ginocchi
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
| | - Judy H Song
- Radiology, Georgetown University, Washington, District of Columbia, USA
| | - Erin Crane
- Radiology, Georgetown University, Washington, District of Columbia, USA
| | - Ranit Mishori
- Family Medicine, Georgetown University, Washington, District of Columbia, USA
| | - Suzanne C O'Neill
- Department of, Oncology, Georgetown University, Washington, District of Columbia, USA
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Hussain S, Ali M, Naseem U, Nezhadmoghadam F, Jatoi MA, Gulliver TA, Tamez-Peña JG. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol 2024; 14:1343627. [PMID: 38571502 PMCID: PMC10987819 DOI: 10.3389/fonc.2024.1343627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Background Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies. Objective and methods This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers. Results A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI. Conclusion This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, QLD, Australia
| | | | - Munsif Ali Jatoi
- Department of Biomedical Engineering, Salim Habib University, Karachi, Pakistan
| | - T. Aaron Gulliver
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
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Eriksson M, Román M, Gräwingholt A, Castells X, Nitrosi A, Pattacini P, Heywang-Köbrunner S, Rossi PG. European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study. THE LANCET REGIONAL HEALTH. EUROPE 2024; 37:100798. [PMID: 38362558 PMCID: PMC10866984 DOI: 10.1016/j.lanepe.2023.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 02/17/2024]
Abstract
Background Image-derived artificial intelligence (AI)-based risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across European screening settings. We therefore investigated the discriminatory performances of an AI-based risk model in European screening settings. Methods Using four European screening populations in three countries (Italy, Spain, Germany) screened between 2009 and 2020 for women aged 45-69, we performed a nested case-control study to assess the predictive performance of an AI-based risk model. In total, 739 women with incident breast cancers were included together with 7812 controls matched on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) were extracted using AI from negative digital mammograms at study-entry. Two-year absolute risks of breast cancer were predicted and assessed after two years of follow-up. Adjusted risk stratification performance metrics were reported per clinical guidelines. Findings The overall adjusted Area Under the receiver operating characteristic Curve (aAUC) of the AI risk model was 0.72 (95% CI 0.70-0.75) for breast cancers developed in four screening populations. In the 6.2% [529/8551] of women at high risk using the National Institute of Health and Care Excellence (NICE) guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95% CI 5.6-8.0), compared with the 69% [5907/8551] of women classified at general risk by the model. Similar risk-ratios were observed across levels of mammographic density. Interpretation The AI risk model showed generalizable discriminatory performances across European populations and, predicted ∼30% of clinically relevant stage 2 and higher breast cancers in ∼6% of high-risk women who were sent home with a negative mammogram. Similar results were seen in women with fatty and dense breasts. Funding Swedish Research Council.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Public Health and Primary Care, University of Cambridge, UK
| | - Marta Román
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | | | - Xavier Castells
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Andrea Nitrosi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
| | | | | | - Paolo G. Rossi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
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Anandarajah A, Chen Y, Stoll C, Hardi A, Jiang S, Colditz GA. Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature. Cancer Causes Control 2023; 34:939-948. [PMID: 37340148 PMCID: PMC10533570 DOI: 10.1007/s10552-023-01739-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 06/14/2023] [Indexed: 06/22/2023]
Abstract
PURPOSE It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk. METHODS The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool. RESULTS Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding. CONCLUSION This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.
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Affiliation(s)
- Akila Anandarajah
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave MSC 8100-0094-2200, Saint Louis, MO, 63110, USA
| | - Yongzhen Chen
- Saint Louis University School of Medicine, Saint Louis, MO, USA
| | - Carolyn Stoll
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave MSC 8100-0094-2200, Saint Louis, MO, 63110, USA
| | - Angela Hardi
- Bernard Becker Medical Library, Washington University School of Medicine, MSC 8132-12-01, 660 S Euclid Ave, Saint Louis, MO, 63110, USA
| | - Shu Jiang
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave MSC 8100-0094-2200, Saint Louis, MO, 63110, USA
| | - Graham A Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, 660 S Euclid Ave MSC 8100-0094-2200, Saint Louis, MO, 63110, USA.
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Yala A, Hughes KS. Rethinking Risk Modeling with Machine Learning. Ann Surg Oncol 2023; 30:6950-6952. [PMID: 37574515 DOI: 10.1245/s10434-023-14144-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Affiliation(s)
- Adam Yala
- UC Berkeley, Berkeley, USA.
- UCSF, San Francisco, USA.
| | - Kevin S Hughes
- Surgical Oncology, Medical University of South Carolina, Charleston, USA
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Kim H, Lim J, Kim HG, Lim Y, Seo BK, Bae MS. Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women. Diagnostics (Basel) 2023; 13:2247. [PMID: 37443642 DOI: 10.3390/diagnostics13132247] [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: 06/01/2023] [Revised: 06/24/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.
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Affiliation(s)
- Hayoung Kim
- Department of Radiology, College of Medicine, Inha University Hospital, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Jihe Lim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Gyeonggi-do, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Seoul 02447, Republic of Korea
| | - Yunji Lim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si 18450, Gyeonggi-do, Republic of Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan-si 15355, Gyeonggi-do, Republic of Korea
| | - Min Sun Bae
- Department of Radiology, College of Medicine, Inha University Hospital, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
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Taylor CR, Monga N, Johnson C, Hawley JR, Patel M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics (Basel) 2023; 13:2041. [PMID: 37370936 DOI: 10.3390/diagnostics13122041] [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: 04/20/2023] [Revised: 05/20/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.
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Affiliation(s)
- Clayton R Taylor
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Natasha Monga
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Candise Johnson
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Jeffrey R Hawley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Mitva Patel
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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