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Gottardelli B, Gatta R, Nucciarelli L, Tudor AM, Tavazzi E, Vallati M, Orini S, Di Giorgi N, Damiani A. GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics. BMC Med Inform Decis Mak 2024; 24:170. [PMID: 38886772 PMCID: PMC11184891 DOI: 10.1186/s12911-024-02549-5] [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: 12/27/2023] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. RESULTS We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. CONCLUSIONS The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.
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Affiliation(s)
- Benedetta Gottardelli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Roberto Gatta
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Leonardo Nucciarelli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy.
| | - Andrada Mihaela Tudor
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Erica Tavazzi
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Mauro Vallati
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Stefania Orini
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
- Alzheimer Operative Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Andrea Damiani
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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2
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Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer 2024; 130:2101-2107. [PMID: 38554271 PMCID: PMC11170282 DOI: 10.1002/cncr.35307] [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: 12/06/2023] [Revised: 02/19/2024] [Accepted: 03/15/2024] [Indexed: 04/01/2024]
Abstract
Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.
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Affiliation(s)
- Likhitha Kolla
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi B. Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
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3
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Jin Z, Wang Y, Han M, Wang L, Lin F, Jia Q, Ren W, Xu J, Yang W, Zhao GA, Sun X, Jing C. Tumor microenvironment-responsive size-changeable and biodegradable HA-CuS/MnO 2 nanosheets for MR imaging and synergistic chemodynamic therapy/phototherapy. Colloids Surf B Biointerfaces 2024; 238:113921. [PMID: 38631280 DOI: 10.1016/j.colsurfb.2024.113921] [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: 02/01/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/19/2024]
Abstract
Tumor microenvironment (TME)-responsive size-changeable and biodegradable nanoplatforms for multimodal therapy possess huge advantages in anti-tumor therapy. Hence, we developed a hyaluronic acid (HA) modified CuS/MnO2 nanosheets (HCMNs) as a multifunctional nanoplatform for synergistic chemodynamic therapy (CDT)/photothermal therapy (PTT)/photodynamic therapy (PDT). The prepared HCMNs exhibited significant NIR light absorption and photothermal conversion efficiency because of the densely deposited ultra-small sized CuS nanoparticles on the surface of MnO2 nanosheet. They could precisely target the tumor cells and rapidly decomposed into small sized nanostructures in the TME, and then efficiently promote intracellular ROS generation through a series of cascade reactions. Moreover, the local temperature elevation induced by photothermal effect also promote the PDT based on CuS nanoparticles and the Fenton-like reaction of Mn2+, thereby enhancing the therapeutic efficiency. Furthermore, the T1-weighted magnetic resonance (MR) imaging was significantly enhanced by the abundant Mn2+ ions from the decomposition process of HCMNs. In addition, the CDT/PTT/PDT synergistic therapy using a single NIR light source exhibited considerable anti-tumor effect via in vitro cell test. Therefore, the developed HCMNs will provide great potential for MR imaging and multimodal synergistic cancer therapy.
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Affiliation(s)
- Zhen Jin
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China; Xinxiang Neural Sensor and Control Engineering Technology Research Center, Xinxiang, Henan 453003, China.
| | - Yunkai Wang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Miaomiao Han
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Li Wang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Fei Lin
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Qianfang Jia
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Wu Ren
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Jiawei Xu
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Wenhao Yang
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Guo-An Zhao
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China.
| | - Xuming Sun
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China; Xinxiang Neural Sensor and Control Engineering Technology Research Center, Xinxiang, Henan 453003, China.
| | - Changqin Jing
- College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan 453003, China.
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4
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Khorsandi D, Rezayat D, Sezen S, Ferrao R, Khosravi A, Zarepour A, Khorsandi M, Hashemian M, Iravani S, Zarrabi A. Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? J Mater Chem B 2024; 12:4584-4612. [PMID: 38686396 DOI: 10.1039/d4tb00310a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.
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Affiliation(s)
- Danial Khorsandi
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
| | - Dorsa Rezayat
- Center for Global Design and Manufacturing, College of Engineering and Applied Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA
| | - Serap Sezen
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956 Istanbul, Türkiye
- Nanotechnology Research and Application Center, Sabanci University, Tuzla 34956 Istanbul, Türkiye
| | - Rafaela Ferrao
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
- University of Coimbra, Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), Portugal
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Türkiye
| | - Atefeh Zarepour
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600 077, India
| | - Melika Khorsandi
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hashemian
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Siavash Iravani
- Independent Researcher, W Nazar ST, Boostan Ave, Isfahan, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye.
- Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan
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5
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Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 2024; 22:411. [PMID: 38702711 PMCID: PMC11069149 DOI: 10.1186/s12967-024-05067-0] [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: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/06/2024] Open
Abstract
Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.
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Affiliation(s)
- Claudio Carini
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, New Hunt's House, King's College London, Guy's Campus, London, UK.
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD, USA.
| | - Attila A Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI, USA.
- Legorreta Cancer Center at Brown University, Providence, RI, USA.
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6
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Ahmed AA, Fawi M, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis. Cancers (Basel) 2024; 16:1506. [PMID: 38672588 PMCID: PMC11048051 DOI: 10.3390/cancers16081506] [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: 03/06/2024] [Revised: 04/04/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists.
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Affiliation(s)
- Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
| | - Muhammad Fawi
- Spider Silk Security DMCC, Dubai 282945, United Arab Emirates
| | - Agnieszka Brychcy
- Department of Clinical Patomorphology, Heliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Martin Witt
- Department of Anatomy, Poznan University of Medical Sciences, 60-806 Poznan, Poland;
- Department of Anatomy, Technische Universität Dresden, 01307 Dresden, Germany
| | - Elżbieta Kaczmarek
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
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7
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Akinsulie OC, Idris I, Aliyu VA, Shahzad S, Banwo OG, Ogunleye SC, Olorunshola M, Okedoyin DO, Ugwu C, Oladapo IP, Gbadegoye JO, Akande QA, Babawale P, Rostami S, Soetan KO. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Front Vet Sci 2024; 11:1347550. [PMID: 38356661 PMCID: PMC10864457 DOI: 10.3389/fvets.2024.1347550] [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: 12/01/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.
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Affiliation(s)
- Olalekan Chris Akinsulie
- Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | - Ibrahim Idris
- Faculty of Veterinary Medicine, Usman Danfodiyo University, Sokoto, Nigeria
| | | | - Sammuel Shahzad
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | | | - Seto Charles Ogunleye
- Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Population Medicine and Pathobiology, College of Veterinary Medicine, Mississippi State University, Starkville, MS, United States
| | - Mercy Olorunshola
- Department of Pharmaceutical Microbiology, University of Ibadan, Ibadan, Nigeria
| | - Deborah O. Okedoyin
- Department of Animal Sciences, North Carolina Agricultural and Technical State University, Greensboro, NC, United States
| | - Charles Ugwu
- College of Veterinary Medicine, Washington State University, Pullman, WA, United States
| | | | - Joy Olaoluwa Gbadegoye
- Department of Physiology, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Qudus Afolabi Akande
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, United States
| | - Pius Babawale
- Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, United States
| | - Sahar Rostami
- Department of Population Medicine and Pathobiology, College of Veterinary Medicine, Mississippi State University, Starkville, MS, United States
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8
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Lippi L, de Sire A, Folli A, Turco A, Moalli S, Marcasciano M, Ammendolia A, Invernizzi M. Obesity and Cancer Rehabilitation for Functional Recovery and Quality of Life in Breast Cancer Survivors: A Comprehensive Review. Cancers (Basel) 2024; 16:521. [PMID: 38339271 PMCID: PMC10854903 DOI: 10.3390/cancers16030521] [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: 12/24/2023] [Revised: 01/12/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
Obesity is a global health challenge with increasing prevalence, and its intricate relationship with cancer has become a critical concern in cancer care. As a result, understanding the multifactorial connections between obesity and breast cancer is imperative for risk stratification, tailored screening, and rehabilitation treatment planning to address long-term survivorship issues. The review follows the SANRA quality criteria and includes an extensive literature search conducted in PubMed/Medline, Web of Science, and Scopus. The biological basis linking obesity and cancer involves complex interactions in adipose tissue and the tumor microenvironment. Various mechanisms, such as hormonal alterations, chronic inflammation, immune system modulation, and mitochondrial dysfunction, contribute to cancer development. The review underlines the importance of comprehensive oncologic rehabilitation, including physical, psychological, and nutritional aspects. Cancer rehabilitation plays a crucial role in managing obesity-related symptoms, offering interventions for physical impairments, pain management, and lymphatic disorders, and improving both physical and psychological well-being. Personalized and technology-driven approaches hold promise for optimizing rehabilitation effectiveness and improving long-term outcomes for obese cancer patients. The comprehensive insights provided in this review contribute to the evolving landscape of cancer care, emphasizing the importance of tailored rehabilitation in optimizing the well-being of obese cancer patients.
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Affiliation(s)
- Lorenzo Lippi
- Department of Health Sciences, University of Eastern Piedmont “A. Avogadro”, 28100 Novara, Italy; (L.L.); (A.F.); (A.T.); (S.M.); (M.I.)
- Translational Medicine, Dipartimento Attività Integrate Ricerca e Innovazione (DAIRI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
| | - Alessandro de Sire
- Department of Medical and Surgical Sciences, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy;
- Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
| | - Arianna Folli
- Department of Health Sciences, University of Eastern Piedmont “A. Avogadro”, 28100 Novara, Italy; (L.L.); (A.F.); (A.T.); (S.M.); (M.I.)
| | - Alessio Turco
- Department of Health Sciences, University of Eastern Piedmont “A. Avogadro”, 28100 Novara, Italy; (L.L.); (A.F.); (A.T.); (S.M.); (M.I.)
| | - Stefano Moalli
- Department of Health Sciences, University of Eastern Piedmont “A. Avogadro”, 28100 Novara, Italy; (L.L.); (A.F.); (A.T.); (S.M.); (M.I.)
| | - Marco Marcasciano
- Experimental and Clinical Medicine Department, Division of Plastic and Reconstructive Surgery, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy;
| | - Antonio Ammendolia
- Department of Medical and Surgical Sciences, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy;
- Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
| | - Marco Invernizzi
- Department of Health Sciences, University of Eastern Piedmont “A. Avogadro”, 28100 Novara, Italy; (L.L.); (A.F.); (A.T.); (S.M.); (M.I.)
- Translational Medicine, Dipartimento Attività Integrate Ricerca e Innovazione (DAIRI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
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9
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Braga L, Lopes R, Alves L, Mota F. The global patent landscape of artificial intelligence applications for cancer. Nat Biotechnol 2023; 41:1679-1687. [PMID: 38082076 DOI: 10.1038/s41587-023-02051-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Affiliation(s)
- Luiza Braga
- Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Luiz Alves
- Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fabio Mota
- Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.
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10
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Weerarathna IN, Kamble AR, Luharia A. Artificial Intelligence Applications for Biomedical Cancer Research: A Review. Cureus 2023; 15:e48307. [PMID: 38058345 PMCID: PMC10697339 DOI: 10.7759/cureus.48307] [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: 10/27/2023] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) has rapidly evolved and demonstrated its potential in transforming biomedical cancer research, offering innovative solutions for cancer diagnosis, treatment, and overall patient care. Over the past two decades, AI has played a pivotal role in revolutionizing various facets of cancer clinical research. In this comprehensive review, we delve into the diverse applications of AI across the cancer care continuum, encompassing radiodiagnosis, radiotherapy, chemotherapy, immunotherapy, targeted therapy, surgery, and nanotechnology. AI has revolutionized cancer diagnosis, enabling early detection and precise characterization through advanced image analysis techniques. In radiodiagnosis, AI-driven algorithms enhance the accuracy of medical imaging, making it an invaluable tool for clinicians in the detection and assessment of cancer. AI has also revolutionized radiotherapy, facilitating precise tumor boundary delineation, optimizing treatment planning, and enabling real-time adjustments to improve therapeutic outcomes while minimizing collateral damage to healthy tissues. In chemotherapy, AI models have emerged as powerful tools for predicting patient responses to different treatment regimens, allowing for more personalized and effective strategies. In immunotherapy, AI analyzes genetic and imaging data to select ideal candidates for treatment and predict responses. Targeted therapy has seen great advancements with AI, aiding in the identification of specific molecular targets for tailored treatments. AI plays a vital role in surgery by offering real-time navigation and support, enhancing surgical precision. Moreover, the synergy between AI and nanotechnology promises the development of personalized nanomedicines, offering more efficient and targeted cancer treatments. While challenges related to data quality, interpretability, and ethical considerations persist, the future of AI in cancer research holds tremendous promise for improving patient outcomes through advanced and individualized care.
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Affiliation(s)
- Induni N Weerarathna
- Biomedical Sciences, School of Allied Health Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aahash R Kamble
- Artificial Intelligence and Data Science, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anurag Luharia
- Radiotherapy, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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11
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Alanzi T, Almahdi R, Alghanim D, Almusmili L, Saleh A, Alanazi S, Alshobaki K, Attar R, Al Qunais A, Alzahrani H, Alshehri R, Sulail A, Alblwi A, Alanzi N, Alanzi N. Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management. Cureus 2023; 15:e49724. [PMID: 38161825 PMCID: PMC10757561 DOI: 10.7759/cureus.49724] [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] [Accepted: 11/25/2023] [Indexed: 01/03/2024] Open
Abstract
AIM AND PURPOSE The purpose of this study is to analyze the various influencing factors affecting the adoption of artificial intelligence (AI)-enabled virtual assistants (VAs) for self-management of leukemia. METHODS A cross-sectional survey design is adopted in this study. The questionnaire included eight factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, and personal innovativeness) affecting the acceptance of AI-enabled virtual assistants. A total of 397 leukemia patients participated in the online survey. RESULTS Performance expectancy (μ = 3.14), effort expectancy (μ = 3.05), and personal innovativeness (μ = 3.14) were identified to be the major influencing factors of AI adoption. Statistically significant differences (p < .05) were observed between the gender-based and age groups of the participants in relation to the various factors. In addition, perceived privacy risks were negatively correlated with all other factors. CONCLUSION Although there are negative factors such as privacy risks and ethical issues in AI adoption, perceived effectiveness and ease of use among individuals are leading to greater adoption of AI-enabled VAs.
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Affiliation(s)
- Turki Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Reham Almahdi
- College of Medicine, Al Baha University, Al Baha, SAU
| | - Danya Alghanim
- College of Medicine and Surgery, Royal College of Surgeons in Ireland, Dublin, IRL
| | | | - Amani Saleh
- Faculty of Pharmacy, Ibnsina National College of Medical Studies, Jeddah, SAU
| | - Sarah Alanazi
- Department of Pharmacy, Almoosa Specialist Hospital, Al Mubarraz, SAU
| | | | - Renad Attar
- College of Medicine, King Abdulaziz University, Jeddah, SAU
| | | | - Haneen Alzahrani
- Department of Hematology, Armed Forces Hospital at King Abdulaziz Airbase Dhahran, Dhahran, SAU
| | | | - Amenah Sulail
- College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Ali Alblwi
- College of Medicine, King Abdulaziz University, Jeddah, SAU
| | - Nawaf Alanzi
- Department of Blood Bank, Regional Laboratory and Blood Banks Arar, Arar, SAU
| | - Nouf Alanzi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf University, Jouf, SAU
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12
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [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: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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13
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Abdallah S, Sharifa M, I Kh Almadhoun MK, Khawar MM, Shaikh U, Balabel KM, Saleh I, Manzoor A, Mandal AK, Ekomwereren O, Khine WM, Oyelaja OT. The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders. Cureus 2023; 15:e46860. [PMID: 37954711 PMCID: PMC10636514 DOI: 10.7759/cureus.46860] [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: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.
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Affiliation(s)
- Shenouda Abdallah
- Surgery, Jaber Al Ahmad Al Jaber Al Sabah Hospital, Kuwait City, KWT
| | | | | | | | - Unzla Shaikh
- Internal Medicine, Liaquat University of Medical and Health Sciences, Hyderabad, PAK
| | | | - Inam Saleh
- Pediatrics, University of Kentucky College of Medicine, Lexington, USA
| | - Amima Manzoor
- Internal Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Arun Kumar Mandal
- General Medicine, Mahawai Basic Hospital/The Oda Foundation, Kalikot, NPL
- Medicine, Manipal College of Medical Sciences, Pokhara, NPL
| | - Osatohanmwen Ekomwereren
- Trauma and Orthopaedics, Royal Shrewsbury Hospital, Shrewsbury and Telford Hospital NHS Trust, Shrewsbury, GBR
| | - Wai Mon Khine
- Internal Medicine, Caribbean Medical School, St. Georges, GRD
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14
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del Campo-Balguerías A, Parra-Cadenas B, Nieto-Jimenez C, Bravo I, Ripoll C, Poyatos-Racionero E, Gancarski P, Carrillo-Hermosilla F, Alonso-Moreno C, Ocaña A. Guanylation Reactions for the Rational Design of Cancer Therapeutic Agents. Int J Mol Sci 2023; 24:13820. [PMID: 37762123 PMCID: PMC10530677 DOI: 10.3390/ijms241813820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 08/29/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023] Open
Abstract
The modular synthesis of the guanidine core by guanylation reactions using commercially available ZnEt2 as a catalyst has been exploited as a tool for the rapid development of antitumoral guanidine candidates. Therefore, a series of phenyl-guanidines were straightforwardly obtained in very high yields. From the in vitro assessment of the antitumoral activity of such structurally diverse guanidines, the guanidine termed ACB3 has been identified as the lead compound of the series. Several biological assays, an estimation of AMDE values, and an uptake study using Fluorescence Lifetime Imaging Microscopy were conducted to gain insight into the mechanism of action. Cell death apoptosis, induction of cell cycle arrest, and reduction in cell adhesion and colony formation have been demonstrated for the lead compound in the series. In this work, and as a proof of concept, we discuss the potential of the catalytic guanylation reactions for high-throughput testing and the rational design of guanidine-based cancer therapeutic agents.
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Affiliation(s)
- Almudena del Campo-Balguerías
- Unidad nanoDrug, Centro Regional de Investigaciones Biomédicas, Universidad de Castilla-La Mancha, 02008 Albacete, Spain; (A.d.C.-B.); (I.B.); (C.R.)
- Departamento Química Inorgánica, Orgánica y Bioquímica, Facultad de Farmacia de Albacete-Centro de Innovación en Química Avanzada (ORFEO-CINQA), Universidad de Castilla-La Mancha, 02008 Albacete, Spain
| | - Blanca Parra-Cadenas
- Departamento de Química Inorgánica, Orgánica y Bioquímica-Centro de Innovación en Química Avanzada (ORFEO-CINQA), Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain; (B.P.-C.); (F.C.-H.)
| | - Cristina Nieto-Jimenez
- Experimental Therapeutics Unit, Hospital Clínico San Carlos, IdISSC, Fundación Jiménez Díaz, START, 28040 Madrid, Spain
| | - Iván Bravo
- Unidad nanoDrug, Centro Regional de Investigaciones Biomédicas, Universidad de Castilla-La Mancha, 02008 Albacete, Spain; (A.d.C.-B.); (I.B.); (C.R.)
- Departamento Química-Física, Facultad de Farmacia de Albacete, Universidad de Castilla-La Mancha, 02008 Albacete, Spain
| | - Consuelo Ripoll
- Unidad nanoDrug, Centro Regional de Investigaciones Biomédicas, Universidad de Castilla-La Mancha, 02008 Albacete, Spain; (A.d.C.-B.); (I.B.); (C.R.)
- Departamento Química-Física, Facultad de Farmacia de Albacete, Universidad de Castilla-La Mancha, 02008 Albacete, Spain
| | | | - Pawel Gancarski
- Cancerappy, Avda Ribera De Axpe, 28, 48950 Erandio, Spain; (E.P.-R.); (P.G.)
| | - Fernando Carrillo-Hermosilla
- Departamento de Química Inorgánica, Orgánica y Bioquímica-Centro de Innovación en Química Avanzada (ORFEO-CINQA), Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain; (B.P.-C.); (F.C.-H.)
| | - Carlos Alonso-Moreno
- Unidad nanoDrug, Centro Regional de Investigaciones Biomédicas, Universidad de Castilla-La Mancha, 02008 Albacete, Spain; (A.d.C.-B.); (I.B.); (C.R.)
- Departamento Química Inorgánica, Orgánica y Bioquímica, Facultad de Farmacia de Albacete-Centro de Innovación en Química Avanzada (ORFEO-CINQA), Universidad de Castilla-La Mancha, 02008 Albacete, Spain
| | - Alberto Ocaña
- Experimental Therapeutics Unit, Hospital Clínico San Carlos, IdISSC, Fundación Jiménez Díaz, START, 28040 Madrid, Spain
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [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: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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16
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Manson EN, Hasford F, Trauernicht C, Ige TA, Inkoom S, Inyang S, Samba O, Khelassi-Toutaoui N, Lazarus G, Sosu EK, Pokoo-Aikins M, Stoeva M. Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way. Phys Med 2023; 113:102653. [PMID: 37586146 DOI: 10.1016/j.ejmp.2023.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.
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Affiliation(s)
| | | | | | | | | | | | - Odette Samba
- General Hospital of Yaoundé and University of Yaoundé I, Cameroon.
| | | | - Graeme Lazarus
- Inkosi Albert Luthuli Central Hospital, Durban, South Africa.
| | - Edem Kwabla Sosu
- School of Nuclear and Allied Sciences, University of Ghana, Ghana.
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17
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He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
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18
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Nazam N, Jabir NR, Ahmad I, Alharthy SA, Khan MS, Ayub R, Tabrez S. Phenolic Acids-Mediated Regulation of Molecular Targets in Ovarian Cancer: Current Understanding and Future Perspectives. Pharmaceuticals (Basel) 2023; 16:274. [PMID: 37259418 PMCID: PMC9962268 DOI: 10.3390/ph16020274] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 11/26/2023] Open
Abstract
Cancer is a global health concern with a dynamic rise in occurrence and one of the leading causes of mortality worldwide. Among different types of cancer, ovarian cancer (OC) is the seventh most diagnosed malignant tumor, while among the gynecological malignancies, it ranks third after cervical and uterine cancer and sadly bears the highest mortality and worst prognosis. First-line treatments have included a variety of cytotoxic and synthetic chemotherapeutic medicines, but they have not been particularly effective in extending OC patients' lives and are associated with side effects, recurrence risk, and drug resistance. Hence, a shift from synthetic to phytochemical-based agents is gaining popularity, and researchers are looking into alternative, cost-effective, and safer chemotherapeutic strategies. Lately, studies on the effectiveness of phenolic acids in ovarian cancer have sparked the scientific community's interest because of their high bioavailability, safety profile, lesser side effects, and cost-effectiveness. Yet this is a road less explored and critically analyzed and lacks the credibility of the novel findings. Phenolic acids are a significant class of phytochemicals usually considered in the nonflavonoid category. The current review focused on the anticancer potential of phenolic acids with a special emphasis on chemoprevention and treatment of OC. We tried to summarize results from experimental, epidemiological, and clinical studies unraveling the benefits of various phenolic acids (hydroxybenzoic acid and hydroxycinnamic acid) in chemoprevention and as anticancer agents of clinical significance.
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Affiliation(s)
- Nazia Nazam
- Amity Institute of Molecular Medicine & Stem Cell Research, Amity University, Noida 201301, Uttar Pradesh, India
| | - Nasimudeen R. Jabir
- Department of Biochemistry, Centre for Research and Development, PRIST University, Vallam, Thanjavur 613403, Tamil Nadu, India
| | - Iftikhar Ahmad
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21362, Saudi Arabia
| | - Saif A. Alharthy
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21362, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohd Shahnawaz Khan
- Department of Biochemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Rashid Ayub
- Technology and Innovation Unit, Department of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Shams Tabrez
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21362, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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