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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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Burger B, Bernathova M, Seeböck P, Singer CF, Helbich TH, Langs G. Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study. Eur Radiol Exp 2023; 7:32. [PMID: 37280478 DOI: 10.1186/s41747-023-00343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/04/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the applicability of deep learning-based anomaly detection to identify anomalous changes in negative breast CE-MRI screens associated with future lesion emergence. METHODS In this prospective study, we trained a generative adversarial network on dynamic CE-MRI of 33 high-risk women who participated in a screening program but did not develop BC. We defined an anomaly score as the deviation of an observed CE-MRI scan from the model of normal breast tissue variability. We evaluated the anomaly score's association with future lesion emergence on the level of local image patches (104,531 normal patches, 455 patches of future lesion location) and entire CE-MRI exams (21 normal, 20 with future lesion). Associations were analyzed by receiver operating characteristic (ROC) curves on the patch level and logistic regression on the examination level. RESULTS The local anomaly score on image patches was a good predictor for future lesion emergence (area under the ROC curve 0.804). An exam-level summary score was significantly associated with the emergence of lesions at any location at a later time point (p = 0.045). CONCLUSIONS Breast cancer lesions are associated with anomalous appearance changes in breast CE-MRI occurring before the lesion emerges in high-risk women. These early image signatures are detectable and may be a basis for adjusting individual BC risk and personalized screening. RELEVANCE STATEMENT Anomalies in screening MRI preceding lesion emergence in women at high-risk of breast cancer may inform individualized screening and intervention strategies. KEY POINTS • Breast lesions are associated with preceding anomalies in CE-MRI of high-risk women. • Deep learning-based anomaly detection can help to adjust risk assessment for future lesions. • An appearance anomaly score may be used for adjusting screening interval times.
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Affiliation(s)
- Bianca Burger
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Computational Imaging Research (CIR), Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Philipp Seeböck
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Computational Imaging Research (CIR), Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Christian F Singer
- Department of Obstetrics and Gynecology, Division of Special Gynecology, Medical University of Vienna, Vienna, Austria
- Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Computational Imaging Research (CIR), Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Alsharif WM. The utilization of artificial intelligence applications to improve breast cancer detection and prognosis. Saudi Med J 2023; 44:119-127. [PMID: 36773967 PMCID: PMC9987701 DOI: 10.15537/smj.2023.44.2.20220611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023] Open
Abstract
Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.
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Affiliation(s)
- Walaa M. Alsharif
- From the Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah; and from the Society of Artificial Intelligence in Healthcare, Riyadh, Kingdom of Saudi Arabia.
- Address correspondence and reprint request to: Dr. Walaa M. Alsharif, Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah, Kingdom of Saudi Arabia. E-mail: ORCID ID: https//:orcid.org/0000-0001-7607-3255
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Yue W, Zhang H, Zhou J, Li G, Tang Z, Sun Z, Cai J, Tian N, Gao S, Dong J, Liu Y, Bai X, Sheng F. Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging. Front Oncol 2022; 12:984626. [PMID: 36033453 PMCID: PMC9404224 DOI: 10.3389/fonc.2022.984626] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI). Materials and methods A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity. Results In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10–15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3. Conclusions Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.
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Affiliation(s)
- Wenyi Yue
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- Chinese PLA General Medical School, Beijing, China
| | - Hongtao Zhang
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Juan Zhou
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Guang Li
- Keya Medical Technology Co., Ltd., Beijing, China
| | - Zhe Tang
- Keya Medical Technology Co., Ltd., Beijing, China
| | - Zeyu Sun
- Keya Medical Technology Co., Ltd., Beijing, China
| | - Jianming Cai
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ning Tian
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shen Gao
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jinghui Dong
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuan Liu
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xu Bai
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fugeng Sheng
- Department of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Fugeng Sheng,
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Galati F, Rizzo V, Trimboli RM, Kripa E, Maroncelli R, Pediconi F. MRI as a biomarker for breast cancer diagnosis and prognosis. BJR Open 2022; 4:20220002. [PMID: 36105423 PMCID: PMC9459861 DOI: 10.1259/bjro.20220002] [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: 01/17/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/05/2022] Open
Abstract
Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.
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Affiliation(s)
- Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Veronica Rizzo
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | | | - Endi Kripa
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Roberto Maroncelli
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, “Sapienza” - University of Rome, Viale Regina Elena, Rome, Italy
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Monsour R, Dutta M, Mohamed AZ, Borkowski A, Viswanadhan NA. Neuroimaging in the Era of Artificial Intelligence: Current Applications. Fed Pract 2022; 39:S14-S20. [PMID: 35765692 PMCID: PMC9227741 DOI: 10.12788/fp.0231] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
BACKGROUND Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI increases efficiency and reduces errors, making it a valuable resource for physicians. With the increasing amount of data processing and image interpretation required, the ability to use AI to augment and aid the radiologist could improve the quality of patient care. OBSERVATIONS AI can predict patient wait times, which may allow more efficient patient scheduling. Additionally, AI can save time for repeat magnetic resonance neuroimaging and reduce the time spent during imaging. AI has the ability to read computed tomography, magnetic resonance imaging, and positron emission tomography with reduced or without contrast without significant loss in sensitivity for detecting lesions. Neuroimaging does raise important ethical considerations and is subject to bias. It is vital that users understand the practical and ethical considerations of the technology. CONCLUSIONS The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI's use for detecting neurologic conditions holds promise in combatting ever increasing imaging volumes and providing timely diagnoses.
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Affiliation(s)
- Robert Monsour
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | - Mudit Dutta
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | | | - Andrew Borkowski
- University of South Florida Morsani College of Medicine, Tampa, Florida
- James A. Haley Veterans’ Hospital, Tampa, Florida
| | - Narayan A. Viswanadhan
- University of South Florida Morsani College of Medicine, Tampa, Florida
- James A. Haley Veterans’ Hospital, Tampa, Florida
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7
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Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2022; 38:527-538. [PMID: 35136337 DOI: 10.12788/fp.0174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. Observations With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. Conclusions AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.
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Affiliation(s)
- L Brannon Thomas
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Stephen M Mastorides
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | | | - Colleen E Jakey
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
| | - Andrew A Borkowski
- James A. Haley Veterans' Hospital, Tampa, Florida.,University of South Florida, Morsani College of Medicine, Tampa
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8
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Lo Gullo R, Wen H, Reiner JS, Hoda R, Sevilimedu V, Martinez DF, Thakur SB, Jochelson MS, Gibbs P, Pinker K. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers (Basel) 2021; 13:cancers13246273. [PMID: 34944898 PMCID: PMC8699819 DOI: 10.3390/cancers13246273] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.
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Affiliation(s)
- Roberto Lo Gullo
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Hannah Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Jeffrey S. Reiner
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Raza Hoda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA;
| | - Danny F. Martinez
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Sunitha B. Thakur
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Peter Gibbs
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Correspondence: ; Tel.: +1-646-888-5200
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Bahl M. Updates in Artificial Intelligence for Breast Imaging. Semin Roentgenol 2021; 57:160-167. [PMID: 35523530 PMCID: PMC9077006 DOI: 10.1053/j.ro.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implementation phase. As of this writing, Food and Drug Administration (FDA)-approved mammographic applications are available for triage, lesion detection and classification, and breast density assessment. For sonography and MRI, FDA-approved applications are available for lesion classification. Numerous other interpretive and noninterpretive AI applications are in the development phase. This article reviews AI applications for mammography, sonography, and MRI that are currently available for clinical use. In addition, clinical implementation and the future of AI for breast imaging are discussed.
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10
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Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021; 142:109882. [PMID: 34392105 PMCID: PMC8387447 DOI: 10.1016/j.ejrad.2021.109882] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022]
Abstract
Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.
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Affiliation(s)
- Almir Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, Sao Paulo, SP, Brazil; Dasa, Sao Paulo, SP, Brazil
| | - Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Guy's and St. Thomas' NHS Trust, Great Maze Pond, London, UK
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, Li J, Ye HR, Cui XW, Dietrich CF. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol 2021; 11:600557. [PMID: 34367938 PMCID: PMC8339920 DOI: 10.3389/fonc.2021.600557] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 07/07/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.
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Affiliation(s)
- Yu-Meng Lei
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Miao Yin
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Mei-Hui Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Jing Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Shu-E Zeng
- Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Jun Li
- Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China
| | - Hua-Rong Ye
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Beau Site, Salem und Permanence, Bern, Switzerland
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