1
|
Riaz IB, Khan MA, Haddad TC. Potential application of artificial intelligence in cancer therapy. Curr Opin Oncol 2024; 36:437-448. [PMID: 39007164 DOI: 10.1097/cco.0000000000001068] [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: 07/16/2024]
Abstract
PURPOSE OF REVIEW This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. RECENT FINDINGS Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. SUMMARY Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.
Collapse
Affiliation(s)
- Irbaz Bin Riaz
- Department of AI and Informatics, Mayo Clinic, Minnesota
- Division of Hematology and Oncology, Mayo Clinic, Phoenix, Arizona
| | | | - Tufia C Haddad
- Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
2
|
McGuinness JE, Anderson GL, Mutasa S, Hershman DL, Terry MB, Tehranifar P, Lew DL, Yee M, Brown EA, Kairouz SS, Kuwajerwala N, Bevers TB, Doster JE, Zarwan C, Kruper L, Minasian LM, Ford L, Arun B, Neuhouser ML, Goodman GE, Brown PH, Ha R, Crew KD. Effects of vitamin D supplementation on a deep learning-based mammographic evaluation in SWOG S0812. JNCI Cancer Spectr 2024; 8:pkae042. [PMID: 38814817 PMCID: PMC11216724 DOI: 10.1093/jncics/pkae042] [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: 12/11/2023] [Revised: 03/04/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
Deep learning-based mammographic evaluations could noninvasively assess response to breast cancer chemoprevention. We evaluated change in a convolutional neural network-based breast cancer risk model applied to mammograms among women enrolled in SWOG S0812, which randomly assigned 208 premenopausal high-risk women to receive oral vitamin D3 20 000 IU weekly or placebo for 12 months. We applied the convolutional neural network model to mammograms collected at baseline (n = 109), 12 months (n = 97), and 24 months (n = 67) and compared changes in convolutional neural network-based risk score between treatment groups. Change in convolutional neural network-based risk score was not statistically significantly different between vitamin D and placebo groups at 12 months (0.005 vs 0.002, P = .875) or at 24 months (0.020 vs 0.001, P = .563). The findings are consistent with the primary analysis of S0812, which did not demonstrate statistically significant changes in mammographic density with vitamin D supplementation compared with placebo. There is an ongoing need to evaluate biomarkers of response to novel breast cancer chemopreventive agents.
Collapse
Affiliation(s)
- Julia E McGuinness
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| | - Garnet L Anderson
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- SWOG Cancer Research Network, Statistics and Data Management Center, Seattle, WA, USA
| | - Simukayi Mutasa
- Department of Radiology, Lenox Hill Hospital, New York, NY, USA
| | - Dawn L Hershman
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| | - Mary Beth Terry
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| | - Parisa Tehranifar
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| | - Danika L Lew
- SWOG Cancer Research Network, Statistics and Data Management Center, Seattle, WA, USA
| | - Monica Yee
- SWOG Cancer Research Network, Statistics and Data Management Center, Seattle, WA, USA
| | - Eric A Brown
- William Beaumont Hospital, Beaumont National Cancer Institute Community Oncology Research Program, Troy, MI, USA
| | - Sebastien S Kairouz
- Cancer Care Specialists of Central Illinois, Heartland National Cancer Institute Community Oncology Research Program, Decatur, IL, USA
| | - Nafisa Kuwajerwala
- William Beaumont Hospital, Beaumont National Cancer Institute Community Oncology Research Program, Troy, MI, USA
| | - Therese B Bevers
- Department of Clinical Cancer Prevention, MD Anderson Cancer Center, Houston, TX, USA
| | - John E Doster
- Anderson Area Cancer Center, Southeast Clinical Oncology Research Consortium National Cancer Institute Community Oncology Research Program, Anderson, SC, USA
| | | | - Laura Kruper
- Department of Breast Oncology, City of Hope Medical Center, Duarte, CA, USA
| | - Lori M Minasian
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Leslie Ford
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Banu Arun
- Department of Clinical Cancer Prevention, MD Anderson Cancer Center, Houston, TX, USA
| | - Marian L Neuhouser
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Gary E Goodman
- Swedish Cancer Institute, Pacific Cancer Research Consortium National Cancer Institute Community Oncology Research Program, Seattle, WA, USA
| | - Powel H Brown
- Department of Clinical Cancer Prevention, MD Anderson Cancer Center, Houston, TX, USA
| | - Richard Ha
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| | - Katherine D Crew
- Department of Medicine, Columbia University Irving Medical Center and the Herbert Irving Comprehensive Cancer Center, New York, NY, USA
| |
Collapse
|
3
|
Al-Karawi D, Al-Zaidi S, Helael KA, Obeidat N, Mouhsen AM, Ajam T, Alshalabi BA, Salman M, Ahmed MH. A Review of Artificial Intelligence in Breast Imaging. Tomography 2024; 10:705-726. [PMID: 38787015 PMCID: PMC11125819 DOI: 10.3390/tomography10050055] [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: 03/05/2024] [Revised: 04/14/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
Collapse
Affiliation(s)
- Dhurgham Al-Karawi
- Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK;
| | - Shakir Al-Zaidi
- Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK;
| | - Khaled Ahmad Helael
- Royal Medical Services, King Hussein Medical Hospital, King Abdullah II Ben Al-Hussein Street, Amman 11855, Jordan;
| | - Naser Obeidat
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Abdulmajeed Mounzer Mouhsen
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Tarek Ajam
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Bashar A. Alshalabi
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Mohamed Salman
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Mohammed H. Ahmed
- School of Computing, Coventry University, 3 Gulson Road, Coventry CV1 5FB, UK;
| |
Collapse
|
4
|
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.
Collapse
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
| | | |
Collapse
|
5
|
Dianati-Nasab M, Salimifard K, Mohammadi R, Saadatmand S, Fararouei M, Hosseini KS, Jiavid-Sharifi B, Chaussalet T, Dehdar S. Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study. Front Oncol 2024; 13:1276232. [PMID: 38425674 PMCID: PMC10903343 DOI: 10.3389/fonc.2023.1276232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/27/2023] [Indexed: 03/02/2024] Open
Abstract
Introduction This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors. Methods The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data. Results The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6. Conclusion This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.
Collapse
Affiliation(s)
- Mostafa Dianati-Nasab
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
- Department of Epidemiology, School of Public Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Khodakaram Salimifard
- Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran
| | - Reza Mohammadi
- Department of Operation Management, Amsterdam Business School, University of Amsterdam, Amsterdam, Netherlands
| | - Sara Saadatmand
- Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran
| | - Mohammad Fararouei
- School of Medical and Life Sciences, Sunway University, Sunway City, Malaysia
| | - Kosar S. Hosseini
- Department of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | | | - Thierry Chaussalet
- Computer Science and Engineering, University of Westminster, London, United Kingdom
| | - Samira Dehdar
- Computational Intelligence & Intelligent Optimization Research Group, Business & Economics School, Persian Gulf University, Bushehr, Iran
| |
Collapse
|
6
|
Schopf CM, Ramwala OA, Lowry KP, Hofvind S, Marinovich ML, Houssami N, Elmore JG, Dontchos BN, Lee JM, Lee CI. Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review. J Am Coll Radiol 2024; 21:319-328. [PMID: 37949155 PMCID: PMC10926179 DOI: 10.1016/j.jacr.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/05/2023] [Accepted: 10/05/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction. MATERIALS AND METHODS A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC). RESULTS Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement. CONCLUSIONS Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.
Collapse
Affiliation(s)
- Cody M Schopf
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Ojas A Ramwala
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Kathryn P Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Solveig Hofvind
- Section Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - M Luke Marinovich
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - Nehmat Houssami
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; National Breast Cancer Foundation Chair in Breast Cancer Prevention at the University of Sydney and Coeditor of The Breast
| | - Joann G Elmore
- David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California; Director of UCLA's National Clinician Scholars Program and Editor-in-Chief of Adult Primary Care at Up-To-Date. https://twitter.com/JoannElmoreMD
| | - Brian N Dontchos
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Clinical Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Janie M Lee
- Section Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of Journal of the American College of Radiology.
| |
Collapse
|
7
|
Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
Collapse
Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| |
Collapse
|
8
|
Siddique M, Liu M, Duong P, Jambawalikar S, Ha R. Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review. Tomography 2023; 9:1110-1119. [PMID: 37368543 DOI: 10.3390/tomography9030091] [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: 03/31/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.
Collapse
Affiliation(s)
- Maham Siddique
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Michael Liu
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Phuong Duong
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Richard Ha
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| |
Collapse
|
9
|
Michel A, Ro V, McGuinness JE, Mutasa S, Terry MB, Tehranifar P, May B, Ha R, Crew KD. Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors. Breast Cancer Res Treat 2023:10.1007/s10549-023-06966-4. [PMID: 37209183 DOI: 10.1007/s10549-023-06966-4] [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: 11/16/2022] [Accepted: 05/03/2023] [Indexed: 05/22/2023]
Abstract
PURPOSE Deep learning techniques, including convolutional neural networks (CNN), have the potential to improve breast cancer risk prediction compared to traditional risk models. We assessed whether combining a CNN-based mammographic evaluation with clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model improved risk prediction. METHODS We conducted a retrospective cohort study among 23,467 women, age 35-74, undergoing screening mammography (2014-2018). We extracted electronic health record (EHR) data on risk factors. We identified 121 women who subsequently developed invasive breast cancer at least 1 year after the baseline mammogram. Mammograms were analyzed with a pixel-wise mammographic evaluation using CNN architecture. We used logistic regression models with breast cancer incidence as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared model prediction performance via area under the receiver operating characteristics curves (AUCs). RESULTS Mean age was 55.9 years (SD, 9.5) with 9.3% non-Hispanic Black and 36% Hispanic. Our hybrid model did not significantly improve risk prediction compared to the BCSC model (AUC of 0.654 vs 0.624, respectively, p = 0.063). In subgroup analyses, the hybrid model outperformed the BCSC model among non-Hispanic Blacks (AUC 0.845 vs. 0.589; p = 0.026) and Hispanics (AUC 0.650 vs 0.595; p = 0.049). CONCLUSION We aimed to develop an efficient breast cancer risk assessment method using CNN risk score and clinical factors from the EHR. With future validation in a larger cohort, our CNN model combined with clinical factors may help predict breast cancer risk in a cohort of racially/ethnically diverse women undergoing screening.
Collapse
Affiliation(s)
- Alissa Michel
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
- Hematology-Oncology, 177 Fort Washington Avenue, New York, NY, 10032, USA.
| | - Vicky Ro
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Julia E McGuinness
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Simukayi Mutasa
- Department of Radiology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Parisa Tehranifar
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Benjamin May
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Ha
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Radiology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Katherine D Crew
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| |
Collapse
|
10
|
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 2023; 186:1772-1791. [PMID: 36905928 DOI: 10.1016/j.cell.2023.01.035] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/10/2023] [Accepted: 01/26/2023] [Indexed: 03/12/2023]
Abstract
Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.
Collapse
|
11
|
Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
Collapse
Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | | | | |
Collapse
|
12
|
Zheng D, He X, Jing J. Overview of Artificial Intelligence in Breast Cancer Medical Imaging. J Clin Med 2023; 12:jcm12020419. [PMID: 36675348 PMCID: PMC9864608 DOI: 10.3390/jcm12020419] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/26/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.
Collapse
|
13
|
Goldberg JE, Reig B, Lewin AA, Gao Y, Heacock L, Heller SL, Moy L. New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis. Radiographics 2023; 43:e220060. [DOI: 10.1148/rg.220060] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Julia E. Goldberg
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Beatriu Reig
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Alana A. Lewin
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Yiming Gao
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Laura Heacock
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Samantha L. Heller
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| | - Linda Moy
- From the Department of Radiology, NYU Langone Health, 550 1st Ave, New York, NY 10016
| |
Collapse
|
14
|
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.
Collapse
|
15
|
Ozcan I, Aydin H, Cetinkaya A. Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer. Asian Pac J Cancer Prev 2022; 23:3287-3297. [PMID: 36308351 PMCID: PMC9924317 DOI: 10.31557/apjcp.2022.23.10.3287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Indexed: 02/18/2023] Open
Abstract
OBJECTIVE To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates. METHODS The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods. RESULT Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%. CONCLUSION When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.
Collapse
Affiliation(s)
- Irem Ozcan
- Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, Turkey.
| | - Hakan Aydin
- Department of Computer Engineering, Faculty of Engineering, Istanbul Topkapı University, Istanbul, Turkey.
| | - Ali Cetinkaya
- Department of Electronics Technology, Istanbul Gelisim Vocational School, Istanbul Gelisim University, Istanbul, Turkey. ,For Correspondence:
| |
Collapse
|
16
|
Abel F, Landsmann A, Hejduk P, Ruppert C, Borkowski K, Ciritsis A, Rossi C, Boss A. Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12061347. [PMID: 35741157 PMCID: PMC9221636 DOI: 10.3390/diagnostics12061347] [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: 04/10/2022] [Revised: 05/26/2022] [Accepted: 05/26/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.
Collapse
|
17
|
Use of a convolutional neural network-based mammographic evaluation to predict breast cancer recurrence among women with hormone receptor-positive operable breast cancer. Breast Cancer Res Treat 2022; 194:35-47. [PMID: 35575954 DOI: 10.1007/s10549-022-06614-3] [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: 01/26/2022] [Accepted: 04/18/2022] [Indexed: 11/02/2022]
Abstract
PURPOSE We evaluated whether a novel, fully automated convolutional neural network (CNN)-based mammographic evaluation can predict breast cancer relapse among women with operable hormone receptor (HR)-positive breast cancer. METHODS We conducted a retrospective cohort study among women with stage I-III, HR-positive unilateral breast cancer diagnosed at Columbia University Medical Center from 2007 to 2017, who received adjuvant endocrine therapy and had at least two mammograms (baseline, annual follow-up) of the contralateral unaffected breast for CNN analysis. We extracted demographics, clinicopathologic characteristics, breast cancer treatments, and relapse status from the electronic health record. Our primary endpoint was change in CNN risk score (range, 0-1). We used two-sample t-tests to assess for difference in mean CNN scores between patients who relapsed vs. remained in remission, and conducted Cox regression analyses to assess for association between change in CNN score and breast cancer-free interval (BCFI), adjusting for known prognostic factors. RESULTS Among 848 women followed for a median of 59 months, there were 67 (7.9%) breast cancer relapses (36 distant, 25 local, 6 new primaries). There was a significant difference in mean absolute change in CNN risk score from baseline to 1-year follow-up between those who relapsed vs. remained in remission (0.001 vs. - 0.022, p = 0.030). After adjustment for prognostic factors, a 0.01 absolute increase in CNN score at 1-year was significantly associated with BCFI, hazard ratio = 1.05 (95% Confidence Interval 1.01-1.09, p = 0.011). CONCLUSION Short-term change in the CNN-based breast cancer risk model on adjuvant endocrine therapy predicts breast cancer relapse, and warrants further evaluation in prospective studies.
Collapse
|
18
|
Bhowmik A, Eskreis-Winkler S. Deep learning in breast imaging. BJR Open 2022; 4:20210060. [PMID: 36105427 PMCID: PMC9459862 DOI: 10.1259/bjro.20210060] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/04/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022] Open
Abstract
Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
Collapse
Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| |
Collapse
|
19
|
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.
Collapse
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.
| |
Collapse
|
20
|
Kizildag Yirgin I, Koyluoglu YO, Seker ME, Ozkan Gurdal S, Ozaydin AN, Ozcinar B, Cabioğlu N, Ozmen V, Aribal E. Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis. Technol Cancer Res Treat 2022; 21:15330338221075172. [PMID: 35060413 PMCID: PMC8796113 DOI: 10.1177/15330338221075172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists’ assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.
Collapse
Affiliation(s)
| | | | | | | | | | - Beyza Ozcinar
- Istanbul University, School of Medicine, Istanbul, Turkey
| | | | - Vahit Ozmen
- Istanbul University, School of Medicine, Istanbul, Turkey
| | - Erkin Aribal
- Acibadem M.A.A University School of Medicine, Istanbul, Turkey
| |
Collapse
|
21
|
Padilla A, Arponen O, Rinta-Kiikka I, Pertuz S. Image retrieval-based parenchymal analysis for breast cancer risk assessment. Med Phys 2021; 49:1055-1064. [PMID: 34837254 DOI: 10.1002/mp.15378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/25/2021] [Accepted: 11/08/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This research on breast cancer risk assessment aims to develop models that predict the likelihood of breast cancer. In recent years, the computerized analysis of visual texture patterns in mammograms, namely parenchymal analysis, has shown great potential for risk assessment. However, the visual complexity and heterogeneity of visual patterns limit the performance of parenchymal analysis in large populations. In this work, we propose a method to create individualized risk assessment models based on the radiological visual appearance (radiomic phenotypes) of the mammograms. METHODS We developed a content-based image retrieval system to stratify mammographic analysis according to the similarities of their radiomic phenotypes. We collected 1144 mammograms from 286 women following a case-control study design. We compared the classical parenchymal analysis with the proposed approach using the area under the ROC curve (AUC) with 95% confidence intervals (CI). Statistical significance was assessed using DeLong's test ( p < 0.05). RESULTS At a patient level, AUC values of 0.504 (95% CI: 0.398-0.611) with classical parenchymal analysis increased to 0.813 (95% CI: 0.734-0.892) when the radiomic phenotypes are incorporated with the proposed method. In risk estimation from individual, standard mammographic views, the highest performance was obtained with the mediolateral oblique view of the right breast (RMLO), with an AUC value of 0.727 (95% CI: 0.634-0.820). Differences in performance among views were statistically significant ( p < 0.05 ) CONCLUSIONS: These results indicate that the utilization of radiomic phenotypes increases the performance of computerized risk assessment based on parenchymal analysis of mammographic images. SIGNIFICANCE The creation of individualized risk assessment models may be leveraged to target personalized screening and prevention recommendations according to the person's risk.
Collapse
Affiliation(s)
- Astrid Padilla
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
| | - Otso Arponen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, 33100, Finland.,Department of Radiology, Tampere University Hospital, Tampere, 33520, Finland
| | - Irina Rinta-Kiikka
- Faculty of Medicine and Health Technology, Tampere University, Tampere, 33100, Finland.,Department of Radiology, Tampere University Hospital, Tampere, 33520, Finland
| | - Said Pertuz
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
| |
Collapse
|
22
|
Tehranifar P, Wei Y, Terry MB. Less Is More-Ways to Move Forward for Improved Breast Cancer Risk Stratification. Cancer Epidemiol Biomarkers Prev 2021; 30:587-589. [PMID: 33811169 DOI: 10.1158/1055-9965.epi-20-1627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 11/16/2022] Open
Abstract
Breast cancer risk models increasingly are including mammographic density (MD) and polygenic risk scores (PRS) to improve identification of higher-risk women who may benefit from genetic screening, earlier and supplemental breast screening, chemoprevention, and other targeted interventions. Here, we present additional considerations for improved clinical use of risk prediction models with MD, PRS, and questionnaire-based risk factors. These considerations include whether changing risk factor patterns, including MD, can improve risk prediction and management, and whether PRS could help inform breast cancer screening without MD measures and prior to the age at initiation of population-based mammography. We further argue that it may be time to reconsider issues around breast cancer risk models that may warrant a more comprehensive head-to-head comparison with other methods for risk factor assessment and risk prediction, including emerging artificial intelligence methods. With the increasing recognition of limitations of any single mathematical model, no matter how simplified, we are at an important juncture for consideration of these different approaches for improved risk stratification in geographically and ethnically diverse populations.See related article by Rosner et al., p. 600.
Collapse
Affiliation(s)
- Parisa Tehranifar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
| | - Ying Wei
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York. .,Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York. .,Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York
| |
Collapse
|
23
|
Jia Z, Luo Y, Wang D, Dinh QN, Lin S, Sharma A, Block EM, Yang M, Gu T, Pearlstein AJ, Yu H, Zhang B. Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network. Biosens Bioelectron 2021; 183:113209. [PMID: 33836430 DOI: 10.1016/j.bios.2021.113209] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/11/2021] [Accepted: 03/28/2021] [Indexed: 01/04/2023]
Abstract
We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L2 regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.
Collapse
Affiliation(s)
- Zhen Jia
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Yaguang Luo
- Environmental Microbial and Food Safety Lab and Food Quality Lab, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, 20705, MD, USA
| | - Dayang Wang
- Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, 01854, MA, USA
| | - Quynh N Dinh
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Sophia Lin
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Arnav Sharma
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, 06269, CT, USA
| | - Ethan M Block
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Manyun Yang
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Tingting Gu
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA
| | - Arne J Pearlstein
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, 01854, MA, USA
| | - Boce Zhang
- Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA.
| |
Collapse
|
24
|
Batchu S, Liu F, Amireh A, Waller J, Umair M. A Review of Applications of Machine Learning in Mammography and Future Challenges. Oncology 2021; 99:483-490. [PMID: 34023831 DOI: 10.1159/000515698] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 03/05/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were searched for relevant literature. Studies evaluating AI models in the context of prediction and diagnosis of breast malignancies that also reported conventional performance metrics were deemed suitable for inclusion. From 90 unique citations, 21 studies were considered suitable for our examination. Data was not pooled due to heterogeneity in study evaluation methods. SUMMARY Three studies showed the applicability of AI in reducing workload. Six studies demonstrated that AI can aid in diagnosis, with up to 69% reduction in false positives and an increase in sensitivity ranging from 84 to 91%. Five studies show how AI models can independently mark and classify suspicious findings on conventional scans, with abilities comparable with radiologists. Seven studies examined AI predictive potential for breast cancer and risk score calculation. Key Messages: Despite limitations in the current evidence base and technical obstacles, this review suggests AI has marked potential for extensive use in mammography. Additional works, including large-scale prospective studies, are warranted to elucidate the clinical utility of AI.
Collapse
Affiliation(s)
- Sai Batchu
- Cooper Medical School of Rowan University, Camden, New Jersey, USA
| | - Fan Liu
- Stanford University School of Medicine, Stanford, California, USA
| | - Ahmad Amireh
- Duke University Medical Center, Durham, North Carolina, USA
| | - Joseph Waller
- Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
| | - Muhammad Umair
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| |
Collapse
|
25
|
Yoon JH, Kim EK. Deep Learning-Based Artificial Intelligence for Mammography. Korean J Radiol 2021; 22:1225-1239. [PMID: 33987993 PMCID: PMC8316774 DOI: 10.3348/kjr.2020.1210] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/11/2021] [Accepted: 01/17/2021] [Indexed: 12/27/2022] Open
Abstract
During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.
Collapse
Affiliation(s)
- Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Seoul, Korea
| | - Eun Kyung Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University, College of Medicine, Yongin, Korea.
| |
Collapse
|
26
|
Ou WC, Polat D, Dogan BE. Deep learning in breast radiology: current progress and future directions. Eur Radiol 2021; 31:4872-4885. [PMID: 33449174 DOI: 10.1007/s00330-020-07640-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/30/2020] [Accepted: 12/17/2020] [Indexed: 12/13/2022]
Abstract
This review provides an overview of current applications of deep learning methods within breast radiology. The diagnostic capabilities of deep learning in breast radiology continue to improve, giving rise to the prospect that these methods may be integrated not only into detection and classification of breast lesions, but also into areas such as risk estimation and prediction of tumor responses to therapy. Remaining challenges include limited availability of high-quality data with expert annotations and ground truth determinations, the need for further validation of initial results, and unresolved medicolegal considerations. KEY POINTS: • Deep learning (DL) continues to push the boundaries of what can be accomplished by artificial intelligence (AI) in breast imaging with distinct advantages over conventional computer-aided detection. • DL-based AI has the potential to augment the capabilities of breast radiologists by improving diagnostic accuracy, increasing efficiency, and supporting clinical decision-making through prediction of prognosis and therapeutic response. • Remaining challenges to DL implementation include a paucity of prospective data on DL utilization and yet unresolved medicolegal questions regarding increasing AI utilization.
Collapse
Affiliation(s)
- William C Ou
- Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, Dallas, TX, 75390, USA.
| | - Dogan Polat
- Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, Dallas, TX, 75390, USA
| | - Basak E Dogan
- Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, Dallas, TX, 75390, USA
| |
Collapse
|
27
|
Manley H, Mutasa S, Chang P, Desperito E, Crew K, Ha R. Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment. Clin Breast Cancer 2020; 21:e312-e318. [PMID: 33277192 DOI: 10.1016/j.clbc.2020.11.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/04/2020] [Accepted: 11/10/2020] [Indexed: 10/23/2022]
Abstract
INTRODUCTION We investigated whether our convolutional neural network (CNN)-based breast cancer risk model is modifiable by testing it on women who had undergone risk-reducing chemoprevention treatment. MATERIALS AND METHODS We conducted a retrospective cohort study of patients diagnosed with atypical hyperplasia, lobular carcinoma in situ, or ductal carcinoma in situ at our institution from 2007 to 2015. The clinical characteristics, chemoprevention use, and mammography images were extracted from the electronic health records. We classified two groups according to chemoprevention use. Mammograms were performed at baseline and subsequent follow-up evaluations for input to our CNN risk model. The 2 chemoprevention groups were compared for the risk score change from baseline to follow-up. The change categories included stayed high risk, stayed low risk, increased from low to high risk, and decreased from high to low risk. Unordered polytomous regression models were used for statistical analysis, with P < .05 considered statistically significant. RESULTS Of 541 patients, 184 (34%) had undergone chemoprevention treatment (group 1) and 357 (66%) had not (group 2). Using our CNN breast cancer risk score, significantly more women in group 1 had shown a decrease in breast cancer risk compared with group 2 (33.7% vs. 22.9%; P < .01). Significantly fewer women in group 1 had an increase in breast cancer risk compared with group 2 (11.4% vs. 20.2%; P < .01). On multivariate analysis, an increase in breast cancer risk predicted by our model correlated negatively with the use of chemoprevention treatment (P = .02). CONCLUSIONS Our CNN-based breast cancer risk score is modifiable with potential utility in assessing the efficacy of known chemoprevention agents and testing new chemoprevention strategies.
Collapse
Affiliation(s)
- Haley Manley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX
| | - Simukayi Mutasa
- Department of Radiology, New York-Presbyterian/Columbia University Medical Center, New York, NY
| | - Peter Chang
- Department of Radiological Sciences and Center for Artificial Intelligence in Diagnostic Medicine, University of California, Irvine, Irvine, CA
| | - Elise Desperito
- Department of Radiology, New York-Presbyterian/Columbia University Medical Center, New York, NY
| | - Katherine Crew
- Departments of Medicine and Epidemiology, New York-Presbyterian/Columbia University Medical Center, New York, NY
| | - Richard Ha
- Department of Radiology and Breast Imaging Section, New York-Presbyterian/Columbia University Medical Center, New York, NY.
| |
Collapse
|
28
|
Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. JOURNAL OF BREAST IMAGING 2020; 2:304-314. [PMID: 32803154 PMCID: PMC7418877 DOI: 10.1093/jbi/wbaa033] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.
Collapse
Affiliation(s)
- Manisha Bahl
- Massachusetts General Hospital, Department of Radiology, Boston, MA
| |
Collapse
|
29
|
Wong DJ, Gandomkar Z, Wu W, Zhang G, Gao W, He X, Wang Y, Reed W. Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review. J Med Radiat Sci 2020; 67:134-142. [PMID: 32134206 PMCID: PMC7276180 DOI: 10.1002/jmrs.385] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 01/18/2020] [Accepted: 02/11/2020] [Indexed: 11/06/2022] Open
Abstract
Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essential task; however, diagnosis can include 'detection' and 'interpretation' errors. Studies to reduce these errors have shown the feasibility of using convolution neural networks (CNNs). This narrative review presents recent studies in diagnosing mammographic malignancy investigating the accuracy and reliability of these CNNs. Databases including ScienceDirect, PubMed, MEDLINE, British Medical Journal and Medscape were searched using the terms 'convolutional neural network or artificial intelligence', 'breast neoplasms [MeSH] or breast cancer or breast carcinoma' and 'mammography [MeSH Terms]'. Articles collected were screened under the inclusion and exclusion criteria, accounting for the publication date and exclusive use of mammography images, and included only literature in English. After extracting data, results were compared and discussed. This review included 33 studies and identified four recurring categories of studies: the differentiation of benign and malignant masses, the localisation of masses, cancer-containing and cancer-free breast tissue differentiation and breast classification based on breast density. CNN's application in detecting malignancy in mammography appears promising but requires further standardised investigations before potentially becoming an integral part of the diagnostic routine in mammography.
Collapse
Affiliation(s)
- Dennis Jay Wong
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Ziba Gandomkar
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Wan‐Jing Wu
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Guijing Zhang
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Wushuang Gao
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Xiaoying He
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Yunuo Wang
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| | - Warren Reed
- Discipline of Medical Imaging SciencesThe University of SydneyLidcombeNew South WalesAustralia
| |
Collapse
|
30
|
Mojahed D, Ha RS, Chang P, Gan Y, Yao X, Angelini B, Hibshoosh H, Taback B, Hendon CP. Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method. Acad Radiol 2020; 27:e81-e86. [PMID: 31324579 DOI: 10.1016/j.acra.2019.06.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/21/2019] [Accepted: 06/24/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND The purpose of this study was to develop a deep learning classification approach to distinguish cancerous from noncancerous regions within optical coherence tomography (OCT) images of breast tissue for potential use in an intraoperative setting for margin assessment. METHODS A custom ultrahigh-resolution OCT (UHR-OCT) system with an axial resolution of 2.7 μm and a lateral resolution of 5.5 μm was used in this study. The algorithm used an A-scan-based classification scheme and the convolutional neural network (CNN) was implemented using an 11-layer architecture consisting of serial 3 × 3 convolution kernels. Four tissue types were classified, including adipose, stroma, ductal carcinoma in situ, and invasive ductal carcinoma. RESULTS The binary classification of cancer versus noncancer with the proposed CNN achieved 94% accuracy, 96% sensitivity, and 92% specificity. The mean five-fold validation F1 score was highest for invasive ductal carcinoma (mean standard deviation, 0.89 ± 0.09) and adipose (0.79 ± 0.17), followed by stroma (0.74 ± 0.18), and ductal carcinoma in situ (0.65 ± 0.15). CONCLUSION It is feasible to use CNN based algorithm to accurately distinguish cancerous regions in OCT images. This fully automated method can overcome limitations of manual interpretation including interobserver variability and speed of interpretation and may enable real-time intraoperative margin assessment.
Collapse
|
31
|
Affiliation(s)
- Manisha Bahl
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
| |
Collapse
|
32
|
Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, Strand F. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction. Radiology 2019; 294:265-272. [PMID: 31845842 DOI: 10.1148/radiol.2019190872] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Background Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers. Conclusion Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Bahl in this issue.
Collapse
Affiliation(s)
- Karin Dembrower
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Yue Liu
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Hossein Azizpour
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Martin Eklund
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Kevin Smith
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Peter Lindholm
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| | - Fredrik Strand
- From the Department of Physiology and Pharmacology, Karolinska Institutet, Solnavägen 9 (Biomedicum), 171 65 Stockholm, Sweden (K.D.); Department of Radiology, Division of Breast Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden (K.D.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (Y.L.); School of Electrical Engineering and Computer, Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden (H.A.); Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden (M.E.); School of Electrical Engineering and Computer, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden (K.S.); Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (P.L.); Department of Thoracic Radiology, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden (P.L.); and Department of Pathology and Oncology, Division of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.)
| |
Collapse
|
33
|
Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT. Abdom Radiol (NY) 2019; 44:2009-2020. [PMID: 30778739 DOI: 10.1007/s00261-019-01929-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
PURPOSE Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. METHODS With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. RESULTS We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. CONCLUSIONS The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.
Collapse
|
34
|
Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data. AJR Am J Roentgenol 2019; 212:1166-1171. [PMID: 30860901 PMCID: PMC8111785 DOI: 10.2214/ajr.18.20250] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE. The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery. MATERIALS AND METHODS. A total of 298 unique images from 149 patients were used for our convolutional neural network algorithm. A total of 134 images from 67 patients with ADH that had been diagnosed by stereotactic-guided biopsy of calcifications but had not been upgraded to ductal carcinoma in situ or invasive cancer at the time of surgical excision. A total of 164 images from 82 patients with mammographic calcifications indicated that ductal carcinoma in situ was the final diagnosis. Two standard mammographic magnification views of the calcifications (a craniocaudal view and a mediolateral or lateromedial view) were used for analysis. Calcifications were segmented using an open-source software platform and images were resized to fit a bounding box of 128 × 128 pixels. A topology with 15 hidden layers was used to implement the convolutional neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Patients were randomly separated into a training-and-validation set (80% of patients) and a test set (20% of patients). Code was implemented using open-source software on a workstation with an open-source operating system and a graphics card. RESULTS. The AUC value was 0.86 (95% CI, ± 0.03) for the test set. Aggregate sensitivity and specificity were 84.6% (95% CI, ± 4.0%) and 88.2% (95% CI, ± 3.0%), respectively. Diagnostic accuracy was 86.7% (95% CI, ± 2.9). CONCLUSION. It is feasible to apply convolutional neural networks to distinguish pure atypical ductal hyperplasia from ductal carcinoma in situ with the use of mammographic images. A larger dataset will likely result in further improvement of our prediction model.
Collapse
|