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Castilho RM, Castilho LS, Palomares BH, Squarize CH. Determinants of Chromatin Organization in Aging and Cancer-Emerging Opportunities for Epigenetic Therapies and AI Technology. Genes (Basel) 2024; 15:710. [PMID: 38927646 PMCID: PMC11202709 DOI: 10.3390/genes15060710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/28/2024] Open
Abstract
This review article critically examines the pivotal role of chromatin organization in gene regulation, cellular differentiation, disease progression and aging. It explores the dynamic between the euchromatin and heterochromatin, coded by a complex array of histone modifications that orchestrate essential cellular processes. We discuss the pathological impacts of chromatin state misregulation, particularly in cancer and accelerated aging conditions such as progeroid syndromes, and highlight the innovative role of epigenetic therapies and artificial intelligence (AI) in comprehending and harnessing the histone code toward personalized medicine. In the context of aging, this review explores the use of AI and advanced machine learning (ML) algorithms to parse vast biological datasets, leading to the development of predictive models for epigenetic modifications and providing a framework for understanding complex regulatory mechanisms, such as those governing cell identity genes. It supports innovative platforms like CEFCIG for high-accuracy predictions and tools like GridGO for tailored ChIP-Seq analysis, which are vital for deciphering the epigenetic landscape. The review also casts a vision on the prospects of AI and ML in oncology, particularly in the personalization of cancer therapy, including early diagnostics and treatment optimization for diseases like head and neck and colorectal cancers by harnessing computational methods, AI advancements and integrated clinical data for a transformative impact on healthcare outcomes.
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Affiliation(s)
- Rogerio M. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
| | - Leonard S. Castilho
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
| | - Bruna H. Palomares
- Oral Diagnosis Department, Piracicaba School of Dentistry, State University of Campinas, Piracicaba 13414-903, Sao Paulo, Brazil;
| | - Cristiane H. Squarize
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, School of Dentistry, University of Michigan, Ann Arbor, MI 48109-1078, USA; (L.S.C.); (C.H.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109-1078, USA
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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Grøvik E. Editorial for "MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model". J Magn Reson Imaging 2024. [PMID: 38217385 DOI: 10.1002/jmri.29226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 01/15/2024] Open
Affiliation(s)
- Endre Grøvik
- Department of Radiology, Division of Diagnostics, Møre and Romsdal Hospital Trust, Ålesund, Norway
- Department of Physics, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim Ålesund Gjøvik, Norway
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Jain Y, Lanjewar R, Shinde RK. Revolutionising Breast Surgery: A Comprehensive Review of Robotic Innovations in Breast Surgery and Reconstruction. Cureus 2024; 16:e52695. [PMID: 38384645 PMCID: PMC10879655 DOI: 10.7759/cureus.52695] [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: 10/13/2023] [Accepted: 01/21/2024] [Indexed: 02/23/2024] Open
Abstract
Robotic innovations in breast surgery have ushered in a new era of precision, safety, and patient-centred care. This comprehensive review explores the multifaceted realm of robotic breast surgery, from preoperative planning to postoperative outcomes, learning curves for surgeons, and the implications for healthcare policies. We examine the ethical considerations, cost-effectiveness, and future directions, including integrating artificial intelligence and telesurgery. Key findings reveal that robotic systems provide improved surgical precision, reduced complications, and enhanced patient satisfaction. Ethical concerns encompass informed consent, resource allocation, and equitable access. The future of breast surgery lies in continued research and development, ensuring that robotics becomes a standard of care accessible to all patients. This technology is reshaping breast surgery and offering new possibilities for minimally invasive, patient-centred care, ultimately redefining the standards of care in this critical field of medicine.
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Affiliation(s)
- Yashraj Jain
- Department of General Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ranjana Lanjewar
- Department of General Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Raju K Shinde
- Department of General Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Singh A, Paruthy SB, Belsariya V, Chandra J N, Singh SK, Manivasagam SS, Choudhary S, Kumar MA, Khera D, Kuraria V. Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus 2023; 15:e50203. [PMID: 38192969 PMCID: PMC10772314 DOI: 10.7759/cureus.50203] [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: 11/09/2023] [Indexed: 01/10/2024] Open
Abstract
Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.
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Affiliation(s)
- Arun Singh
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Shivani B Paruthy
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vivek Belsariya
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Nemi Chandra J
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Sunil Kumar Singh
- Surgical Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | | | - Sushila Choudhary
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - M Anil Kumar
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Dhananjay Khera
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
| | - Vaibhav Kuraria
- General Surgery, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, IND
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Orsini A, Diquigiovanni C, Bonora E. Omics Technologies Improving Breast Cancer Research and Diagnostics. Int J Mol Sci 2023; 24:12690. [PMID: 37628869 PMCID: PMC10454385 DOI: 10.3390/ijms241612690] [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: 06/12/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.
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Affiliation(s)
| | - Chiara Diquigiovanni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40131 Bologna, Italy; (A.O.); (E.B.)
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van den Beukel BAW, de Wilde B, Joosten F, van Goor H, Venderink W, Huisman HJ, Ten Broek RPG. Quantifiable Measures of Abdominal Wall Motion for Quality Assessment of Cine-MRI Slices in Detection of Abdominal Adhesions. J Imaging 2023; 9:jimaging9050092. [PMID: 37233312 DOI: 10.3390/jimaging9050092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
Abdominal adhesions present a diagnostic challenge, and classic imaging modalities can miss their presence. Cine-MRI, which records visceral sliding during patient-controlled breathing, has proven useful in detecting and mapping adhesions. However, patient movements can affect the accuracy of these images, despite there being no standardized algorithm for defining sufficiently high-quality images. This study aims to develop a biomarker for patient movements and determine which patient-related factors influence movement during cine-MRI. Included patients underwent cine-MRI to detect adhesions for chronic abdominal complaints, data were collected from electronic patient files and radiologic reports. Ninety slices of cine-MRI were assessed for quality, using a five-point scale to quantify amplitude, frequency, and slope, from which an image-processing algorithm was developed. The biomarkers closely correlated with qualitative assessments, with an amplitude of 6.5 mm used to distinguish between sufficient and insufficient-quality slices. In multivariable analysis, the amplitude of movement was influenced by age, sex, length, and the presence of a stoma. Unfortunately, no factor was changeable. Strategies for mitigating their impact may be challenging. This study highlights the utility of the developed biomarker in evaluating image quality and providing useful feedback for clinicians. Future studies could improve diagnostic quality by implementing automated quality criteria during cine-MRI.
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Affiliation(s)
| | - Bram de Wilde
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Frank Joosten
- Department of Radiology and Nuclear Medicine, Hospital Rijnstate Arnhem, 6815 AD Arnhem, The Netherlands
| | - Harry van Goor
- Department of Surgery, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Wulphert Venderink
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Henkjan J Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Richard P G Ten Broek
- Department of Surgery, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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