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Yu ZH, Hong YT, Chou CP. Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1372-1380. [PMID: 38897841 DOI: 10.1016/j.ultrasmedbio.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 05/12/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.
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Affiliation(s)
- Zi-Han Yu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Radiology, Jiannren Hospital, Kaohsiung, Taiwan
| | - Yu-Ting Hong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chen-Pin Chou
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan; Department of Pharmacy, College of Pharmacy, Tajen University, Pingtung, Taiwan.
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2
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Zhang J, Dawkins A. Artificial Intelligence in Ultrasound Imaging: Where Are We Now? Ultrasound Q 2024; 40:93-97. [PMID: 38842384 DOI: 10.1097/ruq.0000000000000680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Affiliation(s)
- Jie Zhang
- From the Department of Radiology, University of Kentucky, Lexington, KY
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3
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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.
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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;
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4
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Majumder S, Katz S, Kontos D, Roshkovan L. State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation. BJR Open 2024; 6:tzad004. [PMID: 38352179 PMCID: PMC10860524 DOI: 10.1093/bjro/tzad004] [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: 02/15/2023] [Revised: 09/15/2023] [Accepted: 10/30/2023] [Indexed: 02/16/2024] Open
Abstract
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
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Affiliation(s)
- Shweta Majumder
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Sharyn Katz
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
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5
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [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: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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del Valle Peña Colmenares J, García CC, Velásquez YJV, Pino LAC, Rodríguez ÁG, Rodríguez WJV, Vargas DJG, Herrera DJA. Is using the Gail model to calculate the risk of breast cancer in the Venezuelan population justified? Ecancermedicalscience 2023; 17:1590. [PMID: 37799948 PMCID: PMC10550297 DOI: 10.3332/ecancer.2023.1590] [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: 03/27/2023] [Indexed: 10/07/2023] Open
Abstract
Objective To evaluate the accuracy of the Gail model (GM) in women who already have a diagnosis of breast cancer (BC) from the Breast Pathology Service, Hospital Oncology Department of the Venezuelan Social Security Institute (SOH-IVSS) in the period 2004-2014. To compare the accuracy of the GM in women aged above and below 40 years with a diagnosis of BC. Method Descriptive, retrospective, cross-sectional, 830 records of patients diagnosed with BC were reviewed between 2004 and 2014. Results The mean age for diagnosis of the disease was 46 ± 13 years; menarche age was 13 years ± 2; age at first birth 22 ± 5 years, with a history of biopsy 32 ± 11, the percentage of relatives with a primary history of BC reported (PHBC) 9.3%. Only 41% of women with a diagnosis of BC reported Gail >1.67 (positive Gail). In the dichotomous logistic regression that related positive Gail with the independent variables, it was observed: greater probability of positive Gail if menarche age <11 years (p < 0.036), PHBC (p = 0.005), previous biopsy (p = 0.007), age at first birth 25-29 years (p = 0.019). When stratifying by age, unlike the bivariate analysis, women over 40 years of age are more likely to have a positive Gail in menarche age <11 years (p = 0.008), PHBC (p = 0.001), previous biopsy (p = 0.025) when compared with younger women, the age at first birth between 25 and 29 years was statistically significant for both groups; however, the probability was higher in younger women (p = 0.008). Conclusion There is no conclusive evidence to consider that the GM is applicable to Venezuelan women due to its low precision since it only identified 41% of the patients who had BC as high risk; however, when the factors are analysed separately, we found a higher probability of a positive Gail with statistical significance in EM <11 years, PHBC, previous biopsy and age at first birth 25-29 years; When stratifying by age, we observed that the age at first birth 25-29 years in women aged 40 or less increases the probability of a positive Gail. It is necessary to develop new risk assessment models that are adapted to our female population.
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Affiliation(s)
- Josepmilly del Valle Peña Colmenares
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0002-1114-6289
| | - Carmen Cristina García
- Cátedra de Patología General y Fisiopatología, Escuela Luis Razetti, Facultad de Medicina, Caracas 1050, Venezuela
- https://orcid.org/0000-0002-7889-9445
| | - Yazmin José Velásquez Velásquez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0003-3307-2564
| | - Leider Arelis Campos Pino
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0002-0907-8467
| | - Álvaro Gómez Rodríguez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0003-3740-0238
| | - Wladimir José Villegas Rodríguez
- Servicio Patología Mamaria del Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0001-8999-9751
| | - David José González Vargas
- Servicio Oncológico Hospitalario (SOH), Instituto Venezolano del Seguro Social (IVSS), Caracas 1040, Venezuela
- https://orcid.org/0000-0001-8071-3139
| | - Douglas José Angulo Herrera
- Escuela de Estadística y Ciencias Actuariales, Universidad Central de Venezuela, Caracas 1050, Venezuela
- https://orcid.org/0009-0003-5506-0297
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7
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Zhang M, Mesurolle B, Theriault M, Meterissian S, Morris EA. Imaging of breast cancer-beyond the basics. Curr Probl Cancer 2023:100967. [PMID: 37316336 DOI: 10.1016/j.currproblcancer.2023.100967] [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: 01/12/2023] [Revised: 04/12/2023] [Accepted: 05/20/2023] [Indexed: 06/16/2023]
Abstract
Imaging of breast cancer is the backbone of breast cancer screening, diagnosis, preoperative/treatment assessment and follow-up. The main modalities are mammography, ultrasound and magnetic resonance imaging, each with its own advantages and disadvantages. New emerging technologies have also enabled each modality to improve on their weaknesses. Imaging-guided biopsies have allowed for accurate diagnosis of breast cancer, with low complication rates. The purpose of this article is to review the common modalities for breast cancer imaging in current practice with emphasis on the strengths and potential weaknesses, discuss the selection of the best imaging modality for the specific clinical question or patient population, and explore new technologies / future directions of breast cancer imaging.
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Affiliation(s)
- Michelle Zhang
- Department of Radiology, McGill University Health Center, Montreal, Quebec, Canada.
| | - Benoit Mesurolle
- Department of Radiology, Elsan, Pôle Santé République, Clermont-Ferrand, France
| | - Melanie Theriault
- Department of Radiology, McGill University Health Center, Montreal, Quebec, Canada
| | - Sarkis Meterissian
- Department of Surgery, McGill University Health Centre, Montreal, Quebec, Canada
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8
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Nicosia L, Gnocchi G, Gorini I, Venturini M, Fontana F, Pesapane F, Abiuso I, Bozzini AC, Pizzamiglio M, Latronico A, Abbate F, Meneghetti L, Battaglia O, Pellegrino G, Cassano E. History of Mammography: Analysis of Breast Imaging Diagnostic Achievements over the Last Century. Healthcare (Basel) 2023; 11:healthcare11111596. [PMID: 37297735 DOI: 10.3390/healthcare11111596] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023] Open
Abstract
Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized.
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Affiliation(s)
- Luca Nicosia
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Gnocchi
- Postgraduation School of Diagnostic and Interventional Radiology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Ilaria Gorini
- Centre of Research in Osteoarchaeology and Paleopathology, Department of Biotechnology and Life Sciences, University of Insubria, Via J.H. Dunant, 3, 21100 Varese, Italy
| | - Massimo Venturini
- Diagnostic and Interventional Radiology Department, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy
- School of Medicine and Surgery, Insubria University, 21100 Varese, Italy
| | - Federico Fontana
- Diagnostic and Interventional Radiology Department, Circolo Hospital, ASST Sette Laghi, 21100 Varese, Italy
- School of Medicine and Surgery, Insubria University, 21100 Varese, Italy
| | - Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Ida Abiuso
- Radiology Department, Università degli Studi di Torino, 10129 Turin, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Maria Pizzamiglio
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Antuono Latronico
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Francesca Abbate
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Lorenza Meneghetti
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Ottavia Battaglia
- Postgraduation School of Diagnostic and Interventional Radiology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Giuseppe Pellegrino
- Postgraduation School of Diagnostic and Interventional Radiology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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9
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Chopra S, Khosla M, Vidya R. Innovations and Challenges in Breast Cancer Care: A Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050957. [PMID: 37241189 DOI: 10.3390/medicina59050957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023]
Abstract
Breast cancer care has seen tremendous advancements in recent years through various innovations to improve early detection, diagnosis, treatment, and survival. These innovations include advancements in imaging techniques, minimally invasive surgical techniques, targeted therapies and personalized medicine, radiation therapy, and multidisciplinary care. It is essential to recognize that challenges and limitations exist while significant advancements in breast cancer care exist. Continued research, advocacy, and efforts to address these challenges are necessary to make these innovations accessible to all patients while carefully considering and managing the ethical, social, and practical implications.
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Affiliation(s)
- Sharat Chopra
- Aneurin Bevan University Health Board, The Royal Gwent Hospital, Newport NP20 2UB, UK
| | - Muskaan Khosla
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
| | - Raghavan Vidya
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
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Morgan MB, Mates JL. Ethics of Artificial Intelligence in Breast Imaging. JOURNAL OF BREAST IMAGING 2023; 5:195-200. [PMID: 38416925 DOI: 10.1093/jbi/wbac076] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Indexed: 03/01/2024]
Abstract
There is great interest in the development of artificial intelligence (AI) applications for medical imaging in general and specifically in breast imaging. Because of the scale of application and the potential for harm, there has been a parallel interest in assuring that these new technologies are scrutinized and applied in ethical ways. The four principles of autonomy, beneficence, non-maleficence, and justice are widely accepted as a framework for bioethical analysis. We incorporate a fifth principle of explicability (adapted from Floridi and Cowls) because of the unique considerations of AI. We review definitions of each of these principles and provide examples of their practical application to breast imaging.
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Affiliation(s)
- Matthew B Morgan
- University of Utah School of Medicine, Department of Radiology and Imaging Sciences, Salt Lake City, UT, USA
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11
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Vedantham S, Shazeeb MS, Chiang A, Vijayaraghavan GR. Artificial Intelligence in Breast X-Ray Imaging. Semin Ultrasound CT MR 2023; 44:2-7. [PMID: 36792270 PMCID: PMC9932302 DOI: 10.1053/j.sult.2022.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.
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Affiliation(s)
| | | | - Alan Chiang
- Department of Medical Imaging, University of Arizona, Tucson, AZ
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12
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Syed AH, Khan T. Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis. Front Oncol 2022; 12:854927. [PMID: 36267967 PMCID: PMC9578338 DOI: 10.3389/fonc.2022.854927] [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: 01/14/2022] [Accepted: 08/30/2022] [Indexed: 01/27/2023] Open
Abstract
Objective In recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis. Methodology Therefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work. Results The present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified. Conclusion The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.
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Affiliation(s)
- Asif Hassan Syed
- Department of Computer Science, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Tabrej Khan
- Department of Information Systems, Faculty of Computing and Information Technology Rabigh (FCITR), King Abdulaziz University, Jeddah, Saudi Arabia
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13
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Mansour S, Soliman S, Kansakar A, Marey A, Hunold C, Hanafy MM. Strengths and challenges of the artificial intelligence in the assessment of dense breasts. BJR Open 2022; 4:20220018. [PMID: 38525169 PMCID: PMC10958665 DOI: 10.1259/bjro.20220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/27/2022] [Accepted: 07/20/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. Methods This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Results Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Conclusion Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Advances in knowledge Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.
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Affiliation(s)
| | - Somia Soliman
- Pathology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Abisha Kansakar
- Radiology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Ahmed Marey
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
| | - Christiane Hunold
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
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14
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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] [Grants] [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.
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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
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15
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Ha R, Jairam MP. A review of artificial intelligence in mammography. Clin Imaging 2022; 88:36-44. [DOI: 10.1016/j.clinimag.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/28/2022] [Accepted: 05/12/2022] [Indexed: 11/16/2022]
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16
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Ben M'Barek I, Jauvion G, Ceccaldi PF. [Artificial Intelligence in medicine: What about gynecology-obstetric?]. GYNECOLOGIE, OBSTETRIQUE, FERTILITE & SENOLOGIE 2022; 50:340-343. [PMID: 35183787 DOI: 10.1016/j.gofs.2022.02.075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/17/2022] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Affiliation(s)
- I Ben M'Barek
- Service de gynécologie obstétrique, Assistance publique-Hôpitaux de Paris-Beaujon, 100, boulevard du Général-Leclerc, Clichy, France; Université de Paris, 75006 Paris, France; Département de simulation en Santé, Université de Paris, Paris, France.
| | | | - P-F Ceccaldi
- Service de gynécologie obstétrique, Assistance publique-Hôpitaux de Paris-Beaujon, 100, boulevard du Général-Leclerc, Clichy, France; Université de Paris, 75006 Paris, France; Département de simulation en Santé, Université de Paris, Paris, France
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17
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Artificial Intelligence (AI) for Screening Mammography, From the AI Special Series on AI Applications. AJR Am J Roentgenol 2022; 219:369-380. [PMID: 35018795 DOI: 10.2214/ajr.21.27071] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment, and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.
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18
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Youk JH, Kim EK. Research Highlight: Artificial Intelligence for Ruling Out Negative Examinations in Screening Breast MRI. Korean J Radiol 2022; 23:153-155. [PMID: 35083890 PMCID: PMC8814698 DOI: 10.3348/kjr.2021.0912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 12/17/2021] [Indexed: 12/03/2022] Open
Affiliation(s)
- Ji Hyun Youk
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
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19
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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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20
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Lassau N, Bousaid I, Chouzenoux E, Verdon A, Balleyguier C, Bidault F, Mousseaux E, Harguem-Zayani S, Gaillandre L, Bensalah Z, Doutriaux-Dumoulin I, Monroc M, Haquin A, Ceugnart L, Bachelle F, Charlot M, Thomassin-Naggara I, Fourquet T, Dapvril H, Orabona J, Chamming's F, El Haik M, Zhang-Yin J, Guillot MS, Ohana M, Caramella T, Diascorn Y, Airaud JY, Cuingnet P, Gencer U, Lawrance L, Luciani A, Cotten A, Meder JF. Three artificial intelligence data challenges based on CT and ultrasound. Diagn Interv Imaging 2021; 102:669-674. [PMID: 34312111 DOI: 10.1016/j.diii.2021.06.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers. MATERIALS AND METHODS This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams. The objectives were proposed by the different organizations depending on their core areas of expertise. A dedicated platform was used to upload the medical image data, to automatically anonymize the uploaded data. RESULTS Three challenges were proposed including classification of benign or malignant breast nodules on ultrasound examinations, detection and contouring of pathological neck lymph nodes from cervical CT examinations and classification of calcium score on coronary calcifications from thoracic CT examinations. A total of 2076 medical examinations were included in the database for the three challenges, in three months, by 18 different centers, of which 12% were excluded. The 39 participants were divided into six multidisciplinary teams among which the coronary calcification score challenge was solved with a concordance index > 95%, and the other two with scores of 67% (breast nodule classification) and 63% (neck lymph node calcifications).
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Affiliation(s)
- Nathalie Lassau
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France.
| | - Imad Bousaid
- Direction de la Transformation Numérique et des Systèmes d'Information, Institut Gustave Roussy, 94800 Villejuif, France
| | | | - Antoine Verdon
- Direction de la Transformation Numérique et des Systèmes d'Information, Institut Gustave Roussy, 94800 Villejuif, France
| | - Corinne Balleyguier
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
| | - François Bidault
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
| | - Elie Mousseaux
- Unité Fonctionnelle d'Imagerie Cardiovasculaire Non Invasive, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
| | - Sana Harguem-Zayani
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
| | - Loic Gaillandre
- Centre Libéral d'Imagerie Médicale Agglomération Lille, 59800 Lille, France
| | - Zoubir Bensalah
- Department of Radiology, Centre Hospitalier St Jean, 66000 Perpignan, France
| | | | - Michèle Monroc
- Department of Radiology, Clinique Saint Antoine, 76230 Bois-Guillaume, France
| | - Audrey Haquin
- Department of Radiology, Hôpital de la Croix-Rousse - HCL, 69004 Lyon, France
| | - Luc Ceugnart
- Department of Radiology, Centre Oscar Lambret, 59000 Lille, France
| | | | - Mathilde Charlot
- Department of Radiology, Hôpital Lyon Sud - HCL, 69310 Pierre-Bénite, France
| | | | - Tiphaine Fourquet
- Department of Radiology, Centre Hospitalier Universitaire de Lille, 59000 Lille, France
| | - Héloise Dapvril
- Service d'Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France
| | - Joseph Orabona
- Department of Radiology, Centre Hospitalier de Bastia, 20600 Bastia, France
| | | | - Mickael El Haik
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, 94800 Villejuif, France
| | - Jules Zhang-Yin
- Department of Radiology, Hôpital Tenon, AP-HP, 75020 Paris, France
| | - Marc-Samir Guillot
- Unité Fonctionnelle d'Imagerie Cardiovasculaire Non Invasive, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
| | - Mickaël Ohana
- Department of Radiology, Centre Hospitalier Universitaire de Strasbourg, 67200 Strasbourg, France
| | - Thomas Caramella
- Department of Radiology, Institut Arnault Tzanck, 06700 Saint-Laurent du Var, France
| | - Yann Diascorn
- Department of Radiology, Institut Arnault Tzanck, 06700 Saint-Laurent du Var, France
| | | | - Philippe Cuingnet
- Department of Radiology, Centre Hospitalier de Douai, 59507 Douai, France
| | - Umit Gencer
- Unité Fonctionnelle d'Imagerie Cardiovasculaire Non Invasive, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
| | - Littisha Lawrance
- Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France
| | - Alain Luciani
- Collège des Enseignants de Radiologie de France, 75013 Paris, France; Department of Radiology, Centre Hospitalier Henri Mondor, 94000 Créteil, France
| | - Anne Cotten
- Musculoskeletal Imaging Department, Lille Regional University Hospital, 59000 Lille, France
| | - Jean-François Meder
- Department of Neuroradiology, Centre Hospitalier Sainte-Anne, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
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21
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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: 30] [Impact Index Per Article: 10.0] [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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22
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Grimm LJ. Radiomics: A Primer for Breast Radiologists. JOURNAL OF BREAST IMAGING 2021; 3:276-287. [PMID: 38424774 DOI: 10.1093/jbi/wbab014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Indexed: 03/02/2024]
Abstract
Radiomics has a long-standing history in breast imaging with computer-aided detection (CAD) for screening mammography developed in the late 20th century. Although conventional CAD had widespread adoption, the clinical benefits for experienced breast radiologists were debatable due to high false-positive marks and subsequent increased recall rates. The dramatic growth in recent years of artificial intelligence-based analysis, including machine learning and deep learning, has provided numerous opportunities for improved modern radiomics work in breast imaging. There has been extensive radiomics work in mammography, digital breast tomosynthesis, MRI, ultrasound, PET-CT, and combined multimodality imaging. Specific radiomics outcomes of interest have been diverse, including CAD, prediction of response to neoadjuvant therapy, lesion classification, and survival, among other outcomes. Additionally, the radiogenomics subfield that correlates radiomics features with genetics has been very proliferative, in parallel with the clinical validation of breast cancer molecular subtypes and gene expression assays. Despite the promise of radiomics, there are important challenges related to image normalization, limited large unbiased data sets, and lack of external validation. Much of the radiomics work to date has been exploratory using single-institution retrospective series for analysis, but several promising lines of investigation have made the leap to clinical practice with commercially available products. As a result, breast radiologists will increasingly be incorporating radiomics-based tools into their daily practice in the near future. Therefore, breast radiologists must have a broad understanding of the scope, applications, and limitations of radiomics work.
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Affiliation(s)
- Lars J Grimm
- Duke University, Department of Radiology, Durham, NC, USA
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23
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Goyal S. An Overview of Current Trends, Techniques, Prospects, and Pitfalls of Artificial Intelligence in Breast Imaging. REPORTS IN MEDICAL IMAGING 2021. [DOI: 10.2147/rmi.s295205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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