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Schöder H. Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma. J Clin Oncol 2024:JCO2400675. [PMID: 38905572 DOI: 10.1200/jco.24.00675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/09/2024] [Accepted: 04/16/2024] [Indexed: 06/23/2024] Open
Affiliation(s)
- Heiko Schöder
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, NY
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2
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Weigel S, Katalinic A. [Structured screening for sporadic breast cancer]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:463-470. [PMID: 38499691 DOI: 10.1007/s00117-024-01283-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups. OBJECTIVE Overview of the current state of the MSP, the upcoming age expansion, and potential further developments. MATERIAL AND METHODS Narrative review article with topic-guided literature and data search. RESULTS Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10 years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level. CONCLUSION The MSP has effectively contributed to a reduction of breast cancer mortality. With the expansion of the age groups to 45-75 years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.
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Affiliation(s)
- Stefanie Weigel
- Klinik für Radiologie und Referenzzentrum Mammographie Münster, Universität Münster und Universitätsklinikum Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Deutschland.
| | - Alexander Katalinic
- Institut für Sozialmedizin und Epidemiologie, Universitätsklinikum Schleswig-Holstein, Campus Lübeck und Universität zu Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Deutschland
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Kontos D. The promise of AI in personalized breast cancer screening: are we there yet? Nat Rev Clin Oncol 2024; 21:403-404. [PMID: 38472364 DOI: 10.1038/s41571-024-00877-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Affiliation(s)
- Despina Kontos
- Department of Radiology, Columbia University Irving Medical Center (CUIMC), New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center (CUIMC), New York, NY, USA.
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4
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Sacca L, Lobaina D, Burgoa S, Lotharius K, Moothedan E, Gilmore N, Xie J, Mohler R, Scharf G, Knecht M, Kitsantas P. Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review. J Clin Med 2024; 13:2525. [PMID: 38731054 PMCID: PMC11084581 DOI: 10.3390/jcm13092525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.
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Affiliation(s)
- Lea Sacca
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA; (D.L.); (S.B.); (K.L.); (E.M.); (N.G.); (J.X.); (R.M.); (G.S.); (M.K.); (P.K.)
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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [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: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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6
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Lip G, O'Regan DP. Can machine learning predict cardiac risk using mammography? Eur Heart J Cardiovasc Imaging 2024; 25:467-468. [PMID: 38262145 DOI: 10.1093/ehjci/jeae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 01/13/2024] [Accepted: 01/14/2024] [Indexed: 01/25/2024] Open
Affiliation(s)
- Gerald Lip
- North East of Scotland Breast Screening Program, Foresterhill Road, Aberdeen, UK
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, Du Cane Road, London, W12 0HS, UK
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Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
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Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
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8
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How to support the transition to AI-powered healthcare. Nat Med 2024; 30:609-610. [PMID: 38504014 DOI: 10.1038/s41591-024-02897-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
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9
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Malara N, Coluccio ML, Grillo F, Ferrazzo T, Garo NC, Donato G, Lavecchia A, Fulciniti F, Sapino A, Cascardi E, Pellegrini A, Foxi P, Furlanello C, Negri G, Fadda G, Capitanio A, Pullano S, Garo VM, Ferrazzo F, Lowe A, Torsello A, Candeloro P, Gentile F. Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells. Mol Cancer 2024; 23:32. [PMID: 38350884 PMCID: PMC10863189 DOI: 10.1186/s12943-024-01951-x] [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: 11/09/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation. METHODS a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction. RESULTS the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. CONCLUSION this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.
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Affiliation(s)
- Natalia Malara
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy.
| | - Maria Laura Coluccio
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy
| | - Fabiana Grillo
- Department of Chemistry, University of Leicester, Leicester, UK
| | - Teresa Ferrazzo
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | - Nastassia C Garo
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | - Giuseppe Donato
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | | | | | - Anna Sapino
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo (TO), Turin, Italy
| | - Eliano Cascardi
- Candiolo Cancer Institute, FPO-IRCCS, Candiolo (TO), Turin, Italy
| | - Antonella Pellegrini
- Società Italiana di Citologia (SICi), AO S.Giovanni-Addolorata, President, Roma, IT, Italy
| | - Prassede Foxi
- Cytodiagnostic Pistoia-Pescia Unit, USL Toscana Centro, Pistoia, IT, 51100, Italy
| | | | - Giovanni Negri
- Pathology Unit, Central Hospital Bolzano, via Boehler 5, Bolzano, IT, 39100, Italy
| | - Guido Fadda
- Human Pathology Department, Gaetano Barresi University, Messina, IT, Italy
| | - Arrigo Capitanio
- Linköping University Hospital SE , Linköping University, Linköping, Sweden
| | - Salvatore Pullano
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | - Virginia M Garo
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | - Francesca Ferrazzo
- Department of Health Sciences, University Magna Graecia, Catanzaro, IT, Italy
| | - Alarice Lowe
- Department of Pathology, Stanford University Hospital, Stanford, CA, USA
| | | | - Patrizio Candeloro
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy
| | - Francesco Gentile
- Department of Experimental and Clinical Medicine, University Magna Graecia, Catanzaro, IT, Italy
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van Kooten MJ, Tan CO, Hofmeijer EIS, van Ooijen PMA, Noordzij W, Lamers MJ, Kwee TC, Vliegenthart R, Yakar D. A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist. Insights Imaging 2024; 15:15. [PMID: 38228800 DOI: 10.1186/s13244-023-01595-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/06/2023] [Indexed: 01/18/2024] Open
Abstract
OBJECTIVES To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists. METHODS The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test. RESULTS There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful. CONCLUSION Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization. CRITICAL RELEVANCE STATEMENT The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care. KEY POINTS • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.
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Affiliation(s)
- Maria Jorina van Kooten
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
| | - Can Ozan Tan
- Robotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Elfi Inez Saïda Hofmeijer
- Robotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands
| | - Peter Martinus Adrianus van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Walter Noordzij
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Maria Jolanda Lamers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Thomas Christian Kwee
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
| | - Derya Yakar
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands
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