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Abdullah KM, Sharma G, Singh AP, Siddiqui JA. Nanomedicine in Cancer Therapeutics: Current Perspectives from Bench to Bedside. Mol Cancer 2025; 24:169. [PMID: 40490771 DOI: 10.1186/s12943-025-02368-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2025] [Accepted: 05/26/2025] [Indexed: 06/11/2025] Open
Abstract
Cancer is among the leading causes of death worldwide, with projections indicating that it will claim 35 million lives by the year 2050. Conventional therapies, such as chemotherapy and immune modulation, have reduced cancer mortality to some extent; however, they have limited efficacy due to their broad mode of action, often resulting in cytotoxic effects on normal cells along with the malignant tissues, ultimately limiting their overall optimal therapeutic efficacy outcomes.Rapid advances in nanotechnology and an evolving understanding of cancer mechanisms have propelled the development of a diverse array of nanocarriers to vanquish the hurdles in achieving sophisticated drug delivery with reduced off-target toxicity. Nanoformulations can deliver the anti-cancer agents precisely to the tumor cell by integrating a multitarget approach that allows for tissue-, cell-, or organelle-specific delivery and internalization. Despite the immense interest and unmatched advancements in modern oncology equipped with nanomedicines, only a few nanoformulations have successfully translated into clinical settings. A major reason behind this shortcoming is the lack of a rationale design incorporating smart, responsive targeting features, leading to a compromised therapeutic window due to inefficient internalization or erroneous intracellular localization with unsuccessful payload release. This review aims to summarize the recent perspective of nanomedicine and its translation to clinical practice, with a particular focus on the evolution of strategies used in tumor targeting from traditional EPR-based passive mechanisms to advanced active and multi-stage approaches. We highlight the coupling of organelle-specific and stimuli-responsive nanocarriers, discuss the potential of biomimetic and cell-mediated delivery systems, and also shed light on technologies such as microfluidics, tumor-on-chip models, and AI-assisted synthesis. Finally, this review explores translational hurdles ranging from biological and manufacturing challenges to regulatory bottlenecks and outlines how innovative modeling systems and engineering solutions can bridge the gap from bench to bedside in cancer nanotherapeutics.
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Affiliation(s)
- K M Abdullah
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, 39216, USA
- Cancer Center and Research Institute, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Gunjan Sharma
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, 39216, USA
- Cancer Center and Research Institute, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Ajay P Singh
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, 39216, USA
- Cancer Center and Research Institute, University of Mississippi Medical Center, Jackson, MS, 39216, USA
| | - Jawed A Siddiqui
- Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
- Cancer Center and Research Institute, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
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Aravazhi PS, Gunasekaran P, Benjamin NZY, Thai A, Chandrasekar KK, Kolanu ND, Prajjwal P, Tekuru Y, Brito LV, Inban P. The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions. Dis Mon 2025; 71:101882. [PMID: 40140300 DOI: 10.1016/j.disamonth.2025.101882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2025]
Abstract
BACKGROUND AND OBJECTIVES AI has emerged as a transformative force in clinical medicine, changing the diagnosis, treatment, and management of patients. Tools have been derived for working with ML, DL, and NLP algorithms to analyze large complex medical datasets with unprecedented accuracy and speed, thereby improving diagnostic precision, treatment personalization, and patient care outcomes. For example, CNNs have dramatically improved the accuracy of medical imaging diagnoses, and NLP algorithms have greatly helped extract insights from unstructured data, including EHRs. However, there are still numerous challenges that face AI integration into clinical workflows, including data privacy, algorithmic bias, ethical dilemmas, and problems with the interpretability of "black-box" AI models. These barriers have thus far prevented the widespread application of AI in health care, and its possible trends, obstacles, and future implications are necessary to be systematically explored. The purpose of this paper is, therefore, to assess the current trends in AI applications in clinical medicine, identify those obstacles that are hindering adoption, and identify possible future directions. This research hopes to synthesize evidence from other peer-reviewed articles to provide a more comprehensive understanding of the role that AI plays to advance clinical practices, improve patient outcomes, or enhance decision-making. METHODS A systematic review was done according to the PRISMA guidelines to explore the integration of Artificial Intelligence in clinical medicine, including trends, challenges, and future directions. PubMed, Cochrane Library, Web of Science, and Scopus databases were searched for peer-reviewed articles from 2014 to 2024 with keywords such as "Artificial Intelligence in Medicine," "AI in Clinical Practice," "Machine Learning in Healthcare," and "Ethical Implications of AI in Medicine." Studies focusing on AI application in diagnostics, treatment planning, and patient care reporting measurable clinical outcomes were included. Non-clinical AI applications and articles published before 2014 were excluded. Selected studies were screened for relevance, and then their quality was critically appraised to synthesize data reliably and rigorously. RESULTS This systematic review includes the findings of 8 studies that pointed out the transformational role of AI in clinical medicine. AI tools, such as CNNs, had diagnostic accuracy more than the traditional methods, particularly in radiology and pathology. Predictive models efficiently supported risk stratification, early disease detection, and personalized medicine. Despite these improvements, significant hurdles, including data privacy, algorithmic bias, and resistance from clinicians regarding the "black-box" nature of AI, had yet to be surmounted. XAI has emerged as an attractive solution that offers the promise to enhance interpretability and trust. As a whole, AI appeared promising in enhancing diagnostics, treatment personalization, and clinical workflows by dealing with systemic inefficiencies. CONCLUSION The transformation potential of AI in clinical medicine can transform diagnostics, treatment strategies, and efficiency. Overcoming obstacles such as concerns about data privacy, the danger of algorithmic bias, and difficulties with interpretability may pave the way for broader use and facilitate improvement in patient outcomes while transforming clinical workflows to bring sustainability into healthcare delivery.
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Affiliation(s)
| | | | | | - Andy Thai
- Internal Medicine, Alameda Health System, Highland Hospital, Oakland, USA
| | | | | | | | - Yogesh Tekuru
- RVM Institute of Medical Sciences and Research Center, Laxmakkapally, India
| | | | - Pugazhendi Inban
- Internal Medicine, St. Mary's General Hospital and Saint Clare's Health, NY, USA.
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Sakkal M, Hajal AA. Machine learning predictions of tumor progression: How reliable are we? Comput Biol Med 2025; 191:110156. [PMID: 40245687 DOI: 10.1016/j.compbiomed.2025.110156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 03/06/2025] [Accepted: 04/04/2025] [Indexed: 04/19/2025]
Abstract
BACKGROUND Cancer continues to pose significant challenges in healthcare due to the complex nature of tumor progression. In this digital era, artificial intelligence has emerged as a powerful tool that can potentially transform multiple aspects of cancer care. METHODS In the current study, we conducted a comprehensive literature search across databases such as PubMed, Scopus, and IEEE Xplore. Studies published between 2014 and 2024 were considered. The selection process involved a systematic screening based on predefined inclusion and exclusion criteria. Studies were included if they focused on applying machine learning techniques for tumor progression modeling, diagnosis, or prognosis, were published in peer-reviewed journals or conference proceedings, were available in English, and presented experimental results, simulations, or real-world applications. In total, 87 papers were included in this review, ensuring a diverse and representative analysis of the field. A workflow is included to illustrate the procedure followed to achieve this aim. RESULTS This review delves into the cutting-edge applications of machine learning (ML), including supervised learning methods like Support Vector Machines and Random Forests, as well as advanced deep learning (DL). It focuses on the integration of ML into oncological research, particularly its application in tumor progression through the tumor microenvironment, genetic data, histopathological data, and radiological data. This work provides a critical analysis of the challenges associated with the reliability and accuracy of ML models, which limit their clinical integration. CONCLUSION This review offers expert insights and strategies to address these challenges in order to improve the robustness and applicability of ML in real-world oncology settings. By emphasizing the potential for personalized cancer treatment and bridging gaps between technology and clinical needs, this review serves as a comprehensive resource for advancing the integration of ML models into clinical oncology.
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Affiliation(s)
- Molham Sakkal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Abdallah Abou Hajal
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates; AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates.
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Vyas A, Kumar K, Sharma A, Verma D, Bhatia D, Wahi N, Yadav AK. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med 2025; 191:110178. [PMID: 40228444 DOI: 10.1016/j.compbiomed.2025.110178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2025] [Revised: 04/04/2025] [Accepted: 04/07/2025] [Indexed: 04/16/2025]
Abstract
BACKGROUND Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized. METHOD This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions. RESULTS AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges. CONCLUSIONS AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.
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Affiliation(s)
- Akanksha Vyas
- Academy of Scientific and Innovative Research, Ghaziabad, 201002, India
| | - Krishan Kumar
- Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Ayushi Sharma
- College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan
| | - Damini Verma
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
| | - Dhiraj Bhatia
- Department of Biological Sciences & Engineering, Indian Institute of Technology Gandhinagar, Near Palaj, Gandhinagar, Gujarat, 382355, India
| | - Nitin Wahi
- Department of Biotechnology, LNCT University, Kolar Road, Shirdipuram, Bhopal, Madhya Pradesh, 462042, India
| | - Amit K Yadav
- Department of Biological Sciences & Engineering, Indian Institute of Technology Gandhinagar, Near Palaj, Gandhinagar, Gujarat, 382355, India.
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Sarvepalli S, Vadarevu S. Role of artificial intelligence in cancer drug discovery and development. Cancer Lett 2025; 627:217821. [PMID: 40414522 DOI: 10.1016/j.canlet.2025.217821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Revised: 04/17/2025] [Accepted: 05/23/2025] [Indexed: 05/27/2025]
Abstract
The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to market. AI technologies, such as machine learning (ML) and deep learning (DL), enable the efficient analysis of vast datasets, facilitate faster identification of drug targets, optimization of compounds, and prediction of clinical outcomes. This review explores the multifaceted applications of AI across various stages of cancer drug development, from early-stage discovery to clinical trial design, development. In early-stage discovery, AI-driven methods support target identification, virtual screening (VS), and molecular docking, offering precise predictions that streamline the identification of promising compounds. Additionally, AI is instrumental in de novo drug design and lead optimization, where algorithms can generate novel molecular structures and optimize their properties to enhance drug efficacy and safety profiles. Preclinical development benefits from AI's predictive modeling capabilities, particularly in assessing a drug's toxicity through in silico simulations. AI also plays a pivotal role in biomarker discovery, enabling the identification of specific molecular signatures that can inform patient stratification and personalized treatment approaches. In clinical development, AI optimizes trial design by leveraging real-world data (RWD), improving patient selection, and reducing the time required to bring new drugs to market. Despite its transformative potential, challenges remain, including issues related to data quality, model interpretability, and regulatory hurdles. Addressing these limitations is critical for fully realizing AI's potential in cancer drug discovery and development. As AI continues to evolve, its integration with other technologies, such as genomics and clustered regularly interspaced short palindromic repeats (CRISPR), holds promise for advancing personalized cancer therapies. This review provides a comprehensive overview of AI's impact on the cancer drug discovery and development and highlights future directions for this rapidly evolving field.
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Affiliation(s)
- Sruthi Sarvepalli
- College of Pharmacy and Health Sciences, St. John's University, Queens, NY, USA.
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Dou YN, Wang J. Advancing Oncology Drug Development in the US: The Interplay between Innovations and Regulatory Science. Ther Innov Regul Sci 2025:10.1007/s43441-025-00800-3. [PMID: 40405050 DOI: 10.1007/s43441-025-00800-3] [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: 09/11/2024] [Accepted: 05/07/2025] [Indexed: 05/24/2025]
Abstract
The landscape of drug development has evolved with the adoption of new therapeutic modalities, cutting-edge technology platforms, emerging scientific insights, and modern patient-centric clinical trial designs. In this review, we investigate the interplay between innovation and regulatory science in cancer drug development in the United States. As new innovations emerge, regulatory science adapts to integrate new discoveries and technologies, ensuring alignment with established regulations and safety standards. This fuels additional innovations through data and evidence generation, potentially expediting the development of revolutionary treatments and advancing patient access to novel, promising therapies. Early and frequent engagement with regulators is vital for drug developers aiming to successfully apply innovative approaches.
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Affiliation(s)
- Yannan Nancy Dou
- Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD, USA.
| | - Jian Wang
- Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD, USA.
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Theivendren P, Pavadai P, Kunjiappan S, Ravi K, Kiruthiga N, Chidamabaram K, Alagarsamy S, Reddy NB. Emerging therapeutic strategies and opportunities in targeting protein pathways for breast cancer treatment: a critical review. NANOTECHNOLOGY 2025; 36:232001. [PMID: 40345214 DOI: 10.1088/1361-6528/add6ae] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Accepted: 05/09/2025] [Indexed: 05/11/2025]
Abstract
Understanding breast cancer at a molecular level is essential for developing effective treatments due to its significant impact on women's mortality rates globally. Targeted medicines focus on specific proteins crucial to breast cancer progression, offering a promising treatment avenue. These proteins, often overexpressed or mutated in cancer cells, are vital for cell proliferation, division, and survival. Targeted drugs aim to inhibit these proteins, halting disease progression and sparing non-cancerous cells, which reduces side effects and improves patient quality of life. Key proteins in breast cancer treatment include HER2 (human epidermal growth factor receptor 2), ER (estrogen receptor), and PR (progesterone receptor). Drugs like Trastuzumab target HER2 to impede tumor growth in HER2-positive cancers, while hormone therapies targeting ER and PR improve outcomes for hormone receptor-positive cancers. Examining proteins such as EGFR, HER2/Neu, and ER reveals their roles in cancer pathways, with pathways like PI3K/Akt/mTOR (phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin) and MAPK (mitogen-activated protein kinase) being crucial targets for therapies, potentially revolutionizing breast cancer treatment.
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Affiliation(s)
- Panneerselvam Theivendren
- Department of Pharmaceutical Chemistry & Analysis, School of Pharmaceutical Sciences, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, Tamil Nadu 600117, India
| | - Parasuraman Pavadai
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, M S R Nagar, Bengaluru, Karnataka 560054, India
| | - Selvaraj Kunjiappan
- Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu 626126, India
| | - Kaveena Ravi
- Department of Pharmaceutics, Swamy Vivekananda College of Pharmacy, Elayampalayam, Namakkal, Tamil Nadu 637205, India
| | - Natarajan Kiruthiga
- Department of Pharmaceutical Chemistry, KMCH College of Pharmacy, Kalappatti road, Coimbatore, Tamil Nadu 641048, India
| | - Kumarappan Chidamabaram
- Department of Pharmacology, College of Pharmacy, King Khalid University, Abha 61421, Asir Province, Saudi Arabia
| | - Shanmugarathinam Alagarsamy
- Department of Pharmaceutical Technology, University College of Engineering, Bharathidasan Institute of Technology Campus, Anna University, Tiruchirappalli, Tamil Nadu 620024, India
| | - Nagireddy Bhuvan Reddy
- Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 602 105, India
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Samathoti P, Kumarachari RK, Bukke SPN, Rajasekhar ESK, Jaiswal AA, Eftekhari Z. The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review. Discov Oncol 2025; 16:697. [PMID: 40338421 PMCID: PMC12061837 DOI: 10.1007/s12672-025-02469-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2025] [Accepted: 04/23/2025] [Indexed: 05/09/2025] Open
Abstract
Cancer remains one of the deadliest diseases globally, significantly impacting patients' quality of life. Addressing the rising incidence of cancer deaths necessitates innovative approaches such as nanomedicine and artificial intelligence (AI). The convergence of nanomedicine and AI represents a transformative frontier in cancer healthcare, promising unprecedented advancements in diagnosis, treatment, and patient management. This narrative review explores the distinct applications of nanomedicine and AI in oncology, alongside their synergistic potential. Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. Emerging integrations of these technologies could transform cancer care by facilitating precise, personalized, and adaptive treatment strategies. This review synthesizes current research, highlights innovative individual applications, and discusses the emerging integrations of nanomedicine and AI in oncology. The goal is to provide a comprehensive understanding of how these cutting-edge technologies can collaboratively improve cancer diagnosis, treatment, and patient prognosis.
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Affiliation(s)
- Prasanthi Samathoti
- Department of Pharmaceutics, MB School of Pharmaceutical Sciences (Earst While Sree Vidyanikethan College of Pharmacy), Mohan Babu University, Tirupati, 517102, Andhra Pradesh, India
| | - Rajasekhar Komarla Kumarachari
- Department of Pharmaceutical Chemistry, Meenakshi Faculty of Pharmacy, MAHER University, Thandalam, MevalurKuppam, 602105, Tamil Nadu, India
| | - Sarad Pawar Naik Bukke
- Department of Pharmaceutics and Pharmaceutical Technology, Kampala International University, Western Campus, P.O. Box 71, Ishaka, Bushenyi, Uganda.
| | - Eashwar Sai Komarla Rajasekhar
- Department of Data Science and Artificial Intelligence, Indian Institute of Technology, Bhilai, Kutela Bhata, 491001, Chattisgarh, India
| | | | - Zohre Eftekhari
- Department of Biotechnology, Pasteur Institute of Iran, District 11, Rajabi, M9RW+M55, Tehran, Tehran Province, Iran
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Chen D, He E, Pace K, Chekay M, Raman S. Concordance with SPIRIT-AI guidelines in reporting of randomized controlled trial protocols investigating artificial intelligence in oncology: a systematic review. Oncologist 2025; 30:oyaf112. [PMID: 40421957 PMCID: PMC12107541 DOI: 10.1093/oncolo/oyaf112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2025] [Accepted: 03/24/2025] [Indexed: 05/28/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI) is a promising tool used in oncology that may be able to facilitate diagnosis, treatment planning, and patient management. Transparency and completeness of protocols of randomized controlled trials (RCT) involving AI interventions is necessary to ensure reproducibility of AI tools across diverse clinical settings. The SPIRIT 2013 and SPIRIT-AI 2020 guidelines were developed as evidence-based recommendations for complete reporting of trial protocols. However, the concordance of AI RCT protocols in oncology to SPIRIT reporting guidelines remains unknown. This systematic review evaluates the concordance of protocols of RCTs evaluating AI interventions in oncology to the SPIRIT 2013 and SPIRIT-AI 2020 reporting guidelines. METHODS A systematic search of Ovid Medline and Embase was conducted on October 22, 2024 for primary, peer-reviewed RCT protocols involving AI interventions in oncology. Eligible studies were screened in duplicate and data extraction assessed concordance to SPIRIT 2013 and SPIRIT-AI 2020 guideline items. Item-specific concordance was measured as the proportion of studies that reported the item. Average concordance was measured as the median proportion of items reported for each study. RESULTS Twelve RCT protocols met the inclusion criteria. The median concordance to SPIRIT 2013 guidelines was 81.92% (IQR 74.88-88.95) and the median concordance to SPIRIT-AI 2020 guidelines was 78.21% (IQR 67.21-89.20). For SPIRIT 2013 guidelines, high concordance was observed for items related to study objectives and ethics, but gaps were identified in reporting blinding procedures, participant retention, and post-trial care. For SPIRIT-AI 2020 guidelines, there remained gaps based on data quality management, performance error analysis, and accessibility of AI intervention code. CONCLUSION While concordance to reporting guidelines in oncology AI RCT protocols was moderately high, critical gaps in protocol reporting persist that may hinder reproducibility and clinical implementation. Future efforts should focus on increasing awareness and reinforcement to enhance reporting quality necessary to foster the responsible integration of AI into oncology practice.
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Affiliation(s)
- David Chen
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada M5G 2C4
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3
| | - Emily He
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3
| | - Keiran Pace
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3
| | - Matthew Chekay
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada M5S 3K3
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada M5G 2C4
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada M5T 1P5
- Department of Radiation Oncology, BC Cancer Vancouver, Vancouver, BC, Canada V5Z 1M9
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Chadha S, Mukherjee S, Sanyal S. Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer. Semin Oncol 2025; 52:152349. [PMID: 40345002 DOI: 10.1016/j.seminoncol.2025.152349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2025] [Revised: 03/20/2025] [Accepted: 04/04/2025] [Indexed: 05/11/2025]
Abstract
The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.
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Affiliation(s)
- Sonia Chadha
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, Uttar Pradesh, India.
| | - Sayali Mukherjee
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Somali Sanyal
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, Uttar Pradesh, India
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Kumar R, Singh A, Kassar ASA, Humaida MI, Joshi S, Sharma M. Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia. Front Public Health 2025; 13:1558772. [PMID: 40371275 PMCID: PMC12076014 DOI: 10.3389/fpubh.2025.1558772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2025] [Accepted: 04/07/2025] [Indexed: 05/16/2025] Open
Abstract
In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the efficiency of its delivery. Countries like Saudi Arabia are facing unique adoption challenges in their public healthcare, these challenges are specific to AI literacy, understanding and effective usage of AI technologies. In addition, cultural, regulatory and operational barriers increase the complication of integrating AI literacy into public healthcare operations. In spite of its critical contribution in enabling sustainable healthcare development, limited studies have addressed these adoption challenges. Our study explores the AI literacy adoption barriers in context to Saudi Arabian public healthcare sector, focusing on its relevance for advancing healthcare operations and achieving Sustainable Development Goals (SDGs). The research aims to identifying and addressing the adoption challenges of Artificial Intelligence literacy within the public healthcare in Saudi Arabia. The research aims to enhance the understanding of AI literacy, its necessity for enhancing healthcare operations, and the specific hurdles that impede its successful AI adoption in Saudi Arabia's public healthcare ecosystem. The research employs a qualitative analysis using the T-O-E framework to explore the adoption challenges of AI literacy. Additionally, the Best-Worse Method (BWM) is applied to evaluate the adoption challenges to AI literacy adoption across various operational levels within Saudi Arabia's public healthcare supply chain. The study uncovers substantial adoption challenges at operational, tactical, and strategic level, including institutional readiness, data privacy, and compliance with regulatory frameworks. These challenges complicate the adoption of AI literacy in the Saudi public healthcare supply chains. The research offers critical insights into the various issues affecting the promotion of AI literacy in Saudi Arabia's public healthcare sector. This evidence-based study provides essential commendations for healthcare professionals and policymakers to effectively address the identified challenges, nurturing an environment beneficial to the integration of AI literacy and advancing the goals of sustainable healthcare development.
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Affiliation(s)
- Rakesh Kumar
- Department of Health Management, College of Public Health and Health Informatics, University of Ha’il, Ha’il, Saudi Arabia
| | - Ajay Singh
- Department of Management and Information Systems, College of Business Administration, University of Ha’il, Ha’il, Saudi Arabia
| | - Ahmed Subahi Ahmed Kassar
- Department of Public Health, College of Public Health and Health Informatics, University of Ha’il, Ha’il, Saudi Arabia
| | - Mohammed Ismail Humaida
- Department of Public Health, College of Public Health and Health Informatics, University of Ha’il, Ha’il, Saudi Arabia
| | - Sudhanshu Joshi
- School of Management, Doon University, Dehradun, Uttarakhand, India
| | - Manu Sharma
- Department of Management Studies, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
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Adugna A, Amare GA, Jemal M. Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma. Cancer Inform 2025; 24:11769351251333847. [PMID: 40291818 PMCID: PMC12033511 DOI: 10.1177/11769351251333847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Accepted: 03/24/2025] [Indexed: 04/30/2025] Open
Abstract
Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.
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Affiliation(s)
- Adane Adugna
- Medical Laboratory Sciences, College of Health Sciences, Debre Markos University, Ethiopia
| | - Gashaw Azanaw Amare
- Medical Laboratory Sciences, College of Health Sciences, Debre Markos University, Ethiopia
| | - Mohammed Jemal
- Department of Biomedical Sciences, School of Medicine, Debre Markos University, Ethiopia
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13
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Miao R, Zhong BJ, Mei XY, Dong X, Ou YD, Liang Y, Yu HY, Wang Y, Dong ZH. A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction. Front Genet 2025; 16:1532651. [PMID: 40191608 PMCID: PMC11968432 DOI: 10.3389/fgene.2025.1532651] [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: 12/02/2024] [Accepted: 02/24/2025] [Indexed: 04/09/2025] Open
Abstract
Motivation Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become the core problem of precision oncology. At present, drug response prediction using multi-omics gene data faces the following three main challenges: first, how to design a gene probe feature extraction model with biological interpretation and high performance; second, how to develop multi-omics weighting modules for reasonably fusing genetic data of different lengths and noise conditions; third, how to construct deep learning models that can handle small sample sizes while minimizing the risk of possible overfitting. Results We propose an innovative drug response prediction model (NMDP). First, the NMDP model introduces an interpretable semi-supervised weighted SPCA module to solve the feature extraction problem in multi-omics gene data. Next, we construct a multi-omics data fusion framework based on sample similarity networks, bimodal tests, and variance information, which solves the data fusion problem and enables the NMDP model to focus on more relevant genomic data. Finally, we combine a one-dimensional convolution method and Kolmogorov-Arnold networks (KANs) to predict the drug response. We conduct five sets of real data experiments and compare NMDP against seven advanced drug response prediction methods. The results show that NMDP achieves the best performance, with sensitivity and specificity reaching 0.92 and 0.93, respectively-an improvement of 11%-57% compared to other models. Bio-enrichment experiments strongly support the biological interpretation of the NMDP model and its ability to identify potential targets for drug activity prediction.
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Affiliation(s)
- Rui Miao
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Bing-Jie Zhong
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Xin-Yue Mei
- Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
| | - Xin Dong
- Institute of Systems Engineering, Macau University of Science and Technology, Macau, China
| | - Yang-Dong Ou
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, China
| | | | - Hao-Yang Yu
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Ying Wang
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
| | - Zi-Han Dong
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China
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14
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Saadh MJ, Ahmed HH, Kareem RA, Yadav A, Ganesan S, Shankhyan A, Sharma GC, Naidu KS, Rakhmatullaev A, Sameer HN, Yaseen A, Athab ZH, Adil M, Farhood B. Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling. Discov Oncol 2025; 16:334. [PMID: 40095253 PMCID: PMC11914415 DOI: 10.1007/s12672-025-02111-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2024] [Accepted: 03/10/2025] [Indexed: 03/19/2025] Open
Abstract
PURPOSE This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques. MATERIALS AND METHODS A dataset of 1759 samples (987 breast cancer patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, and ElasticNet for feature selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, and transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability. Classifiers such as XGBoost, LightGBM, ensemble voting, Multi-Layer Perceptron, and Stacking were trained using grid search and cross-validation. Model evaluation was conducted using accuracy, AUC, MCC, Kappa Score, ROC, and PR curves, with external validation performed on an independent dataset of 175 samples. RESULTS XGBoost and LightGBM achieved the highest test accuracies (0.91 and 0.90) and AUC values (up to 0.92), particularly with NMF and BioBERT. The ensemble Voting method exhibited the best external accuracy (0.92), confirming its robustness. Transformer-based embeddings and advanced feature selection techniques significantly improved model performance compared to conventional approaches like PCA and Decision Trees. CONCLUSION The proposed ML framework enhances diagnostic accuracy and interpretability, demonstrating strong generalizability on an external dataset. These findings highlight its potential for precision oncology and personalized breast cancer diagnostics.
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Affiliation(s)
- Mohamed J Saadh
- Faculty of Pharmacy, Middle East University, Amman, 11831, Jordan
| | | | | | - Anupam Yadav
- Department of Computer Engineering and Application, GLA University, Mathura, 281406, India
| | - Subbulakshmi Ganesan
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India
| | - Aman Shankhyan
- Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India
| | - Girish Chandra Sharma
- Department of Applied Sciences-Chemistry, NIMS Institute of Engineering and Technology, NIMS University Rajasthan, Jaipur, India
| | - K Satyam Naidu
- Department of Chemistry, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, 531162, India
| | - Akmal Rakhmatullaev
- Department of Faculty Pediatric Surgery, Tashkent Pediatric Medical Institute, Bogishamol Street 223, 100140, Tashkent, Uzbekistan
| | - Hayder Naji Sameer
- Collage of Pharmacy, National University of Science and Technology, Dhi Qar, 64001, Iraq
| | | | - Zainab H Athab
- Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq
| | - Mohaned Adil
- Pharmacy College, Al-Farahidi University, Baghdad, Iraq
| | - Bagher Farhood
- Department of Medical Physics and Radiology, Faculty of Paramedical Sciences, Kashan University of Medical Sciences, Kashan, Iran.
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15
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Alum EU. AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis. Discov Oncol 2025; 16:313. [PMID: 40082367 PMCID: PMC11906928 DOI: 10.1007/s12672-025-02064-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Accepted: 03/05/2025] [Indexed: 03/16/2025] Open
Abstract
Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly in developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise in cancer cases. Despite the potential to mitigate the adverse effects of cancer by early detection and the implementation of cancer prevention methods, several nations have limited screening facilities. In oncology, the use of artificial intelligence (AI) represents a transformative advancement in cancer diagnosis, prognosis, and treatment. The use of AI in biomarker discovery improves precision medicine by uncovering biomarker signatures that are essential for early detection and treatment of diseases within vast and diverse datasets. Deep learning and machine learning diagnostics are two examples of AI technologies that are changing the way biomarkers are made by finding patterns in large datasets and making new technologies that make it possible to deliver accurate and effective therapies. Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. The advancement of biomarker discovery methodologies with AI seeks to transform cancer by improving patient survival rates through enhanced early diagnosis and targeted therapy. This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. Data from reputable scientific databases such as PubMed, Scopus, and ScienceDirect were utilized.
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Affiliation(s)
- Esther Ugo Alum
- Department of Research and Publications, Kampala International University, P. O. Box 20000, Kampala, Uganda.
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16
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Chang TG, Park S, Schäffer AA, Jiang P, Ruppin E. Hallmarks of artificial intelligence contributions to precision oncology. NATURE CANCER 2025; 6:417-431. [PMID: 40055572 PMCID: PMC11957836 DOI: 10.1038/s43018-025-00917-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Accepted: 01/21/2025] [Indexed: 03/29/2025]
Abstract
The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.
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Affiliation(s)
- Tian-Gen Chang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Seongyong Park
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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17
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Azadinejad H, Farhadi Rad M, Shariftabrizi A, Rahmim A, Abdollahi H. Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy. Diagnostics (Basel) 2025; 15:397. [PMID: 39941326 PMCID: PMC11816985 DOI: 10.3390/diagnostics15030397] [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: 12/10/2024] [Revised: 01/14/2025] [Accepted: 01/22/2025] [Indexed: 02/16/2025] Open
Abstract
Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.
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Affiliation(s)
- Hossein Azadinejad
- Department of Immunology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah 6714869914, Iran;
| | - Mohammad Farhadi Rad
- Radiology and Nuclear Medicine Department, School of Paramedical Sciences, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran
| | - Ahmad Shariftabrizi
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Hamid Abdollahi
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada
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18
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Hamamoto R, Komatsu M, Yamada M, Kobayashi K, Takahashi M, Miyake M, Jinnai S, Koyama T, Kouno N, Machino H, Takahashi S, Asada K, Ueda N, Kaneko S. Current status and future direction of cancer research using artificial intelligence for clinical application. Cancer Sci 2025; 116:297-307. [PMID: 39557634 PMCID: PMC11786316 DOI: 10.1111/cas.16395] [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: 08/31/2024] [Revised: 10/23/2024] [Accepted: 11/01/2024] [Indexed: 11/20/2024] Open
Abstract
The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI-equipped software as a medical device (AI-SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data-driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI-based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology.
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Affiliation(s)
- Ryuji Hamamoto
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Masaaki Komatsu
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Masayoshi Yamada
- Department of EndoscopyNational Cancer Center HospitalTokyoJapan
| | - Kazuma Kobayashi
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Masamichi Takahashi
- Department of Neurosurgery and Neuro‐OncologyNational Cancer Center HospitalTokyoJapan
- Department of Neurosurgery, School of MedicineTokai UniversityIseharaKanagawaJapan
| | - Mototaka Miyake
- Department of Diagnostic RadiologyNational Cancer Center HospitalTokyoJapan
| | - Shunichi Jinnai
- Department of Dermatologic OncologyNational Cancer Center Hospital EastKashiwaJapan
| | - Takafumi Koyama
- Department of Experimental TherapeuticsNational Cancer Center HospitalTokyoJapan
| | - Nobuji Kouno
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
- Department of Surgery, Graduate School of MedicineKyoto UniversityKyotoJapan
| | - Hidenori Machino
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Satoshi Takahashi
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Ken Asada
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Naonori Ueda
- Disaster Resilience Science TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Syuzo Kaneko
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research TeamRIKEN Center for Advanced Intelligence ProjectTokyoJapan
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19
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Olawade DB, Clement David-Olawade A, Adereni T, Egbon E, Teke J, Boussios S. Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions. Diseases 2025; 13:24. [PMID: 39851488 PMCID: PMC11764268 DOI: 10.3390/diseases13010024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2024] [Revised: 01/12/2025] [Accepted: 01/17/2025] [Indexed: 01/26/2025] Open
Abstract
BACKGROUND Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness. OBJECTIVE This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets. METHODS A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic. RESULTS AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies. CONCLUSIONS AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.
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Affiliation(s)
- David B. Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UK;
- Department of Public Health, York St John University, London E14 2BA, UK
| | | | - Temitope Adereni
- Department of Public Health, University of Dundee, Dundee DD1 4HN, UK;
| | - Eghosasere Egbon
- Department of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, Austria;
| | - Jennifer Teke
- Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UK;
- Department of Surgery, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UK
- Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UK;
| | - Stergios Boussios
- Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UK;
- Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand, London WC2R 2LS, UK
- Kent Medway Medical School, University of Kent, Canterbury, Kent CT2 7LX, UK
- AELIA Organization, 9th Km Thessaloniki—Thermi, 57001 Thessaloniki, Greece
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UK
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20
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El-Tanani M, Rabbani SA, El-Tanani Y, Matalka II, Khalil IA. Bridging the gap: From petri dish to patient - Advancements in translational drug discovery. Heliyon 2025; 11:e41317. [PMID: 39811269 PMCID: PMC11730937 DOI: 10.1016/j.heliyon.2024.e41317] [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: 09/28/2024] [Revised: 12/13/2024] [Accepted: 12/17/2024] [Indexed: 01/11/2025] Open
Abstract
Translational research serves as the bridge between basic research and practical applications in clinical settings. The journey from "bench to bedside" is fraught with challenges and complexities such as the often-observed disparity between how compounds behave in a laboratory setting versus in the complex systems of living organisms. The challenge is further compounded by the limited ability of in vitro models to mimic the specific biochemical environment of human tissues. This article explores and details the recent advancements and innovative approaches that are increasingly successful in bridging the gap between laboratory research and patient care. These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. The article aims to showcase how these technologies improve the predictability of drug performance in human bodies and significantly speed up the drug development process. Furthermore, it discusses the role of biomarker discovery in preparation of more targeted and personalized therapy approaches and covers the impact of regulatory changes designed to facilitate drug approvals. Additionally, by providing detailed case studies of successful applications, we illustrate the practical impacts of these innovations on drug discovery and patient care.
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Affiliation(s)
- Mohamed El-Tanani
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates
| | - Syed Arman Rabbani
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates
| | | | - Ismail I. Matalka
- Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates
- Department of Pathology and Microbiology, Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Ikramy A. Khalil
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates
- Faculty of Pharmacy, Assiut University, Assiut, 71526, Egypt
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21
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Xu M, Chen Y, Wu T, Chen Y, Zhuang W, Huang Y, Chen C. Global research trends in the application of artificial intelligence in oncology care: a bibliometric study. Front Oncol 2025; 14:1456144. [PMID: 39839779 PMCID: PMC11746057 DOI: 10.3389/fonc.2024.1456144] [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: 06/28/2024] [Accepted: 12/09/2024] [Indexed: 01/23/2025] Open
Abstract
Objective To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers. Methods The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences. Results A total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were "model" and "human papillomavirus." The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates. Conclusion By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.
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Affiliation(s)
- Mianmian Xu
- Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, China
| | - Yafang Chen
- Department of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Tianen Wu
- Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, China
| | - Yuyan Chen
- Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, China
| | - Wanling Zhuang
- Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, China
| | - Yinhui Huang
- Department of Neurology, Jinjiang Municipal Hospital, Quanzhou, China
| | - Chuanzhen Chen
- Department of Nursing, Jinjiang Municipal Hospital, Quanzhou, China
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22
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Alsaiari AA. Recent advances in the methods and clinical applications of next-generation sequencing in genomic profiling and precision cancer therapy. EXCLI JOURNAL 2025; 24:15-33. [PMID: 39967910 PMCID: PMC11830917 DOI: 10.17179/excli2024-7594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Accepted: 10/17/2024] [Indexed: 02/20/2025]
Abstract
Cancer is a major cause of death worldwide. Next-generation sequencing (NGS) has dramatically increased the sequencing data output and transformed biomedical investigations. NGS enables the generations of genetic data specific to patients from tumor tissue samples so that targeted therapies can be used. The obtained data further allows the prioritization of effective therapies based on the tumor-specific genotype. Practitioners in the field of clinical genomics can make the best use of testing facilities while lessening the possible off-targets by choosing a priori gene set. Therefore, targeted sequencing has arisen as a more affordable technique for the genomic profiling of tumors. Drug resistance is commonly observed in cancer because of mutations. Thus, precise genetic and molecular profiling of tumors ought to be routinely done prior to the use of targeted therapy or precision cancer therapy. NGS already has the capacity to ameliorate genetic screening in families with previous histories of the high occurrence of various cancer-associated genes, including TP53, APC, BRCA2, and BRCA1. By using NGS system, researchers detected increased variants in cancer cells with greater specificity and sensitivity than conventional diagnostic approaches, which suggest the potential of NGS in diagnosis. The field of precision cancer therapy is continuously growing and because of their specificity at the molecular level has improved the management and treatment of numerous cancers. These therapies are less toxic and more efficient compared to conventional chemotherapies used in cancer treatment. The field of precision cancer therapy is likely to significantly expand as NGS system advances. This review provides extensive information regarding current advances in the NGS field in terms of methods, clinical applications, genomic profiling, and the role of NGS of precision cancer therapy.
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Affiliation(s)
- Ahad Amer Alsaiari
- Department of Clinical Laboratory Science, College of Applied Medical Science, Taif University, Taif, Saudi Arabia
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23
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Chang L, Liu J, Zhu J, Guo S, Wang Y, Zhou Z, Wei X. Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization. Cancer Biol Med 2025; 22:j.issn.2095-3941.2024.0376. [PMID: 39749734 PMCID: PMC11795263 DOI: 10.20892/j.issn.2095-3941.2024.0376] [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: 09/18/2024] [Accepted: 11/27/2024] [Indexed: 01/04/2025] Open
Abstract
Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI's roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.
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Affiliation(s)
- Luchen Chang
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
| | - Jiamei Liu
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
| | - Jialin Zhu
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
| | - Shuyue Guo
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
| | - Yao Wang
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
| | - Zhiwei Zhou
- Departments of Biochemistry and Radiation Oncology, UT Southwestern Medical Center, Dallas 75390, USA
| | - Xi Wei
- Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
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Long Z, Zhang L. Detection of Hepatocellular Carcinoma Using Optimized miRNA Combinations and Interpretable Machine Learning Models. IEEE ACCESS 2025; 13:66078-66093. [DOI: 10.1109/access.2025.3559105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2025]
Affiliation(s)
- Zhengwu Long
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Lisheng Zhang
- Bio-Medical Center, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei, China
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Uwimana A, Gnecco G, Riccaboni M. Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review. Comput Biol Med 2025; 184:109391. [PMID: 39579663 DOI: 10.1016/j.compbiomed.2024.109391] [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: 05/15/2024] [Revised: 10/01/2024] [Accepted: 11/07/2024] [Indexed: 11/25/2024]
Abstract
BACKGROUND Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor. OBJECTIVES This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA). METHODS We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice. RESULTS Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems. CONCLUSION In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.
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Affiliation(s)
| | | | - Massimo Riccaboni
- IMT School for Advanced Studies, Lucca, Italy; IUSS University School for Advanced Studies, Pavia, Italy.
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Gaffney H, Mirza KM. Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight. Acad Pathol 2025; 12:100166. [PMID: 40104157 PMCID: PMC11919318 DOI: 10.1016/j.acpath.2025.100166] [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: 05/01/2024] [Revised: 01/08/2025] [Accepted: 01/09/2025] [Indexed: 03/20/2025] Open
Abstract
The integration of artificial intelligence in pathology has ignited discussions about the role of technology in diagnostics-whether artificial intelligence serves as a tool for augmentation or risks replacing human expertise. This manuscript explores artificial intelligence's evolving contributions to pathology, emphasizing its potential capacity to enhance, rather than eclipse, the pathologist's role. Through historical comparisons, such as the transition from analog to digital in radiology, this paper highlights how technological advancements have historically expanded professional capabilities without diminishing the essential human element. Current applications of artificial intelligence in pathology-from diagnostic standardization to workflow efficiency-demonstrate its potential to augment diagnostic accuracy, expedite processes, and improve consistency across institutions. However, challenges remain in algorithmic bias, regulatory oversight, and maintaining interpretive skills among pathologists. The discussion underscores the importance of comprehensive governance frameworks, evolving educational curricula, and public engagement initiatives to ensure artificial intelligence in pathology remains a collaborative endeavor that empowers professionals, upholds ethical standards, and enhances patient outcomes. This manuscript ultimately advocates for a balanced approach where artificial intelligence and human expertise work in concert to advance the future of diagnostic medicine.
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Affiliation(s)
- Harry Gaffney
- Concord Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Kamran M Mirza
- The Godfrey D. Stobbe Professor of Pathology Education, Assistant Chair for Education and Director of the Division of Training, Programs and Communication, University of Michigan (Michigan Medicine) Department of Pathology, Ann Arbor, MI, USA
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Mazumdar H, Khondakar KR, Das S, Halder A, Kaushik A. Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes. Expert Opin Drug Deliv 2025; 22:85-108. [PMID: 39645588 DOI: 10.1080/17425247.2024.2440618] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2024] [Revised: 11/15/2024] [Accepted: 12/06/2024] [Indexed: 12/09/2024]
Abstract
INTRODUCTION Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes. AREAS COVERED Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care. EXPERT OPINION Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.
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Affiliation(s)
- Hirak Mazumdar
- Department of Computer Science and Engineering, Adamas University, Kolkata, India
| | | | - Suparna Das
- Department of Computer Science and Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India
| | - Animesh Halder
- Department of Electrical and Electronics Engineering, Adamas University, Kolkata, India
| | - Ajeet Kaushik
- Nano Biotech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL, USA
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Stathopoulos I, Stoklasa R, Kouri MA, Velonakis G, Karavasilis E, Efstathopoulos E, Serio L. Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis. J Imaging 2024; 11:6. [PMID: 39852319 PMCID: PMC11766070 DOI: 10.3390/jimaging11010006] [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: 12/09/2024] [Revised: 12/23/2024] [Accepted: 12/24/2024] [Indexed: 01/26/2025] Open
Abstract
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH). We present the segmentation results for both the whole abnormal volume and for each abnormal component inside the examinations of the validation set. In the first case, a dice score coefficient (DSC), sensitivity, and precision of 0.76, 0.78, and 0.82, respectively, were found, while in the second case the model detected and segmented correct (True positives) the 48.8% (DSC ≥ 0.5) of abnormal components, partially correct the 27.1% (0.05 > DSC > 0.5), and missed (False Negatives) the 24.1%, while it produced 25.1% False Positives. Finally, we present an extended analysis between the True positives, False Negatives, and False positives versus their position inside the brain, their intensity at three MRI modalities (FLAIR, T2, and T1ce) and their volume.
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Affiliation(s)
- Ioannis Stathopoulos
- 2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.S.); (M.A.K.); (G.V.)
- Technology Department, CERN, 1211 Geneva, Switzerland; (R.S.); (L.S.)
| | - Roman Stoklasa
- Technology Department, CERN, 1211 Geneva, Switzerland; (R.S.); (L.S.)
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, 60200 Brno, Czech Republic
| | - Maria Anthi Kouri
- 2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.S.); (M.A.K.); (G.V.)
| | - Georgios Velonakis
- 2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.S.); (M.A.K.); (G.V.)
| | - Efstratios Karavasilis
- Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece;
| | - Efstathios Efstathopoulos
- 2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.S.); (M.A.K.); (G.V.)
| | - Luigi Serio
- Technology Department, CERN, 1211 Geneva, Switzerland; (R.S.); (L.S.)
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Al Shahrani A, Alhumaidan N, AlHindawi Z, Althobaiti A, Aloufi K, Almughamisi R, Aldalbahi A. Readiness to Embrace Artificial Intelligence Among Medical Students in Saudi Arabia: A National Survey. Healthcare (Basel) 2024; 12:2504. [PMID: 39765931 PMCID: PMC11727990 DOI: 10.3390/healthcare12242504] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 12/06/2024] [Accepted: 12/07/2024] [Indexed: 01/06/2025] Open
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence (AI) is rapidly reshaping healthcare, offering transformative potential for diagnostics, treatment, and patient management. Despite its growing significance, there is limited integration of AI education in medical curricula, raising concerns about the readiness of future healthcare professionals to utilize AI technologies. This study aims to evaluate the readiness of medical students in Saudi Arabia to embrace AI and to assess the current state of AI education, AI Application use, and future perspectives for medical students. METHODS a cross-sectional design was employed. It involved medical students from various regions of Saudi Arabia. Data were collected using an anonymous, online, structured, and validated tool from previous studies. The survey included sociodemographic information, details on AI education, the usage of AI applications, intended specialties, and a Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). The data were extracted and revised in an Excel sheet. Statistical analysis was conducted using the IBM SPSS computer program with appropriate statistical tests. RESULTS This study enrolled 572 medical students, with a mean age of 21.93 years. Most students were Saudi (99.0%), and 43.7% lived in the western region of Saudi Arabia. Most students attended a government medical college (97.41%), and 64.3% of students were in their clinical years. Only 14.5% of the students had received formal AI education, while 34.3% had participated in extracurricular AI training. The mean (SD) MAIRS-MS score was 68.39 (18.3), with higher scores associated with female students, those from the central region, and those with advanced English and computer technology skills (p < 0.001). CONCLUSIONS there is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background. These findings underscore the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.
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Affiliation(s)
- Abeer Al Shahrani
- Family and Community Medicine Department, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Norah Alhumaidan
- College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia; (N.A.); (Z.A.)
| | - Zeena AlHindawi
- College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia; (N.A.); (Z.A.)
| | | | - Khalid Aloufi
- College of Medicine, Northern Border University, Arar 73213, Saudi Arabia;
| | - Rasil Almughamisi
- College of Medicine, Taibah University, Al Madinah Al Munawwarah 42353, Saudi Arabia;
| | - Ahad Aldalbahi
- College of Medicine, King Faisal University, Hofuf 31982, Saudi Arabia;
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30
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Obeagu EI, Obeagu GU. Predictive models and biomarkers for survival in stage III breast cancer: a review of clinical applications and future directions. Ann Med Surg (Lond) 2024; 86:5980-5987. [PMID: 39359789 PMCID: PMC11444610 DOI: 10.1097/ms9.0000000000002517] [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: 07/01/2024] [Accepted: 08/19/2024] [Indexed: 10/04/2024] Open
Abstract
Stage III breast cancer, characterized by locally advanced tumors and potential regional lymph node involvement, presents a formidable challenge to both patients and healthcare professionals. Accurate prediction of survival outcomes is crucial for guiding treatment decisions and optimizing patient care. This publication explores the potential clinical utility of predictive tools, encompassing genetic markers, imaging techniques, and clinical parameters, to improve survival outcome predictions in stage III breast cancer. Multimodal approaches, integrating these tools, hold the promise of delivering more precise and personalized predictions. Despite the inherent challenges, such as data standardization and genetic heterogeneity, the future offers opportunities for refinement, driven by precision medicine, artificial intelligence, and global collaboration. The goal is to empower healthcare providers to make informed treatment decisions, ultimately leading to improved survival outcomes and a brighter horizon for individuals facing this challenging disease.
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Wu J, Li R, Gan J, Zheng Q, Wang G, Tao W, Yang M, Li W, Ji G, Li W. Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population. Thorac Cancer 2024; 15:2061-2072. [PMID: 39206529 PMCID: PMC11444925 DOI: 10.1111/1759-7714.15428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application. METHODS Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules. RESULTS The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001). CONCLUSIONS Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.
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Affiliation(s)
- Jiaxuan Wu
- Department of Pulmonary and Critical Care Medicine, West China HospitalSichuan UniversityChengduSichuanChina
- State Key Laboratory of Respiratory Health and MultimorbidityWest China HospitalChengduSichuanChina
- Institute of Respiratory Health and Multimorbidity, West China HospitalSichuan UniversityChengduSichuanChina
| | - Ruicen Li
- Health Management Center, General Practice Medical Center, West China HospitalSichuan UniversityChengduChina
| | - Jiadi Gan
- Department of Pulmonary and Critical Care Medicine, West China HospitalSichuan UniversityChengduSichuanChina
- State Key Laboratory of Respiratory Health and MultimorbidityWest China HospitalChengduSichuanChina
- Institute of Respiratory Health and Multimorbidity, West China HospitalSichuan UniversityChengduSichuanChina
| | - Qian Zheng
- West China Clinical Medical CollegeSichuan UniversityChengduChina
| | - Guoqing Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan UniversityChengduSichuanChina
| | - Wenjuan Tao
- Institute of Hospital Management, West China HospitalSichuan UniversityChengduChina
| | - Ming Yang
- National Clinical Research Center for Geriatrics (WCH), West China HospitalSichuan UniversityChengduChina
- Center of Gerontology and Geriatrics, West China HospitalSichuan UniversityChengduChina
| | - Wenyu Li
- Health Management Center, General Practice Medical Center, West China HospitalSichuan UniversityChengduChina
| | - Guiyi Ji
- Health Management Center, General Practice Medical Center, West China HospitalSichuan UniversityChengduChina
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China HospitalSichuan UniversityChengduSichuanChina
- State Key Laboratory of Respiratory Health and MultimorbidityWest China HospitalChengduSichuanChina
- Institute of Respiratory Health and Multimorbidity, West China HospitalSichuan UniversityChengduSichuanChina
- Institute of Respiratory Health, Frontiers Science Center for Disease‐related Molecular Network, West China HospitalSichuan UniversityChengduSichuanChina
- Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China HospitalSichuan UniversityChengduSichuanChina
- The Research Units of West China, Chinese Academy of Medical SciencesWest China HospitalChengduSichuanChina
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Jawdekar R, Mishra V, Hatgoankar K, Tiwade YR, Bankar NJ. Precision medicine in cancer treatment: Revolutionizing care through proteomics, genomics, and personalized therapies. J Cancer Res Ther 2024; 20:1687-1693. [DOI: 10.4103/jcrt.jcrt_108_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 08/20/2024] [Indexed: 01/03/2025]
Abstract
ABSTRACT
Recent developments in biotechnology have allowed us to identify unique and complicated biological traits associated with cancer. Genomic profiling through next-generation sequencing (NGS) has revolutionized cancer therapy by evaluating hundreds of genes and biomarkers in a single assay. Proteomics offers blood-based biomarkers for cancer detection, categorization, and therapy monitoring. Immune oncology and chimeric antigen receptor (CAR-T cell) therapy use the immune system to combat cancer. Personalized cancer treatment is on the rise. Although precision medicine holds great promise, its widespread application faces obstacles such as lack of agreement on nomenclature, the difficulty of classifying patients into distinct groups, the difficulties of multimorbidity, magnitude, and the need for prompt intervention. This review studies advances in the era of precision medicine for cancer treatment; the application of genomic profiling techniques, NGS, proteomics, and targeted therapy; and the challenge in the application of precision medicine and the beneficial future it holds in cancer treatment.
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Affiliation(s)
- Riddhi Jawdekar
- Department of Pathology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, Maharashtra, India
| | - Vaishnavi Mishra
- Department of Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India
| | - Kajal Hatgoankar
- Department of Pathology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, Maharashtra, India
| | - Yugeshwari R. Tiwade
- Department of Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India
| | - Nandkishor J. Bankar
- Department of Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India
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Brahimllari O, Eloranta S, Georgii-Hemming P, Haider Z, Koch S, Krstic A, Skarp FP, Rosenquist R, Smedby KE, Taylan F, Thorvaldsdottir B, Wirta V, Wästerlid T, Boman M. Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis. Health Informatics J 2024; 30:14604582241290725. [PMID: 39394057 DOI: 10.1177/14604582241290725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2024]
Abstract
Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.
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Affiliation(s)
- Orlinda Brahimllari
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sandra Eloranta
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Zahra Haider
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Sabine Koch
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Aleksandra Krstic
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | | | - Richard Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Karin E Smedby
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Clinical Genetics, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Birna Thorvaldsdottir
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Valtteri Wirta
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Genomic Medicine Center Karolinska, Karolinska University Hospital, Stockholm, Sweden
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tove Wästerlid
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Magnus Boman
- MedTechLabs, BioClinicum, Karolinska University Hospital, Stockholm, Sweden
- Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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Chen M, Wang T, Tian D, Hai C, Qiu Z. Induction, growth, drug resistance, and metastasis: A comprehensive summary of the relationship between STAT3 and gastric cancer. Heliyon 2024; 10:e37263. [PMID: 39309860 PMCID: PMC11416542 DOI: 10.1016/j.heliyon.2024.e37263] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/23/2024] [Accepted: 08/29/2024] [Indexed: 09/25/2024] Open
Abstract
Gastric cancer is a prevalent and highly lethal malignancy that poses substantial challenges to healthcare systems globally. Owing to its often asymptomatic nature in early stages, diagnosis frequently occurs at advanced stages when surgical intervention is no longer a viable option, forcing most patients to rely on nonsurgical treatments such as chemotherapy, targeted therapies, and emerging immunotherapies. Unfortunately, the therapeutic response rates for these treatments are suboptimal, and even among responders, the eventual development of drug resistance remains a significant clinical hurdle. Signal transducer and activator of transcription 3 (STAT3) is a widely expressed cellular protein that plays crucial roles in regulating cellular processes such as growth, metabolism, and immune function. Aberrant activation of the STAT3 pathway has been implicated in the initiation, progression, and therapeutic resistance of several cancers, with gastric cancer being particularly affected. Dysregulated STAT3 signaling not only drives tumorigenesis but also facilitates the development of resistance to chemotherapy and targeted therapies, as well as promotes metastatic dissemination. In this study, we explored the critical role of the STAT3 signaling cascade in the pathogenesis of gastric cancer, its contribution to drug resistance, and its involvement in the metastatic process. Furthermore, we assess recent advances in the development of STAT3 inhibitors and their potential application as therapeutic agents in the treatment of gastric cancer. This work provides a comprehensive overview of the current understanding of STAT3 in gastric cancer and offers a foundation for future research aimed at improving therapeutic outcomes in this challenging disease.
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Affiliation(s)
- Muyang Chen
- School of Pediatrics, Nanjing Medical University, Nanjing, China
| | - Tongshan Wang
- Gastric Cancer Center, Department of Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Dianzhe Tian
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chaorui Hai
- School of Life Sciences, Lanzhou University, Lanzhou, China
| | - Zixuan Qiu
- School of Public Health, Xiangya School of Medicine, Central South University, Changsha, China
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Kamble P, Nagar PR, Bhakhar KA, Garg P, Sobhia ME, Naidu S, Bharatam PV. Cancer pharmacoinformatics: Databases and analytical tools. Funct Integr Genomics 2024; 24:166. [PMID: 39294509 DOI: 10.1007/s10142-024-01445-5] [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: 07/29/2024] [Revised: 08/26/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.
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Affiliation(s)
- Pradnya Kamble
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prinsa R Nagar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Kaushikkumar A Bhakhar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - M Elizabeth Sobhia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Srivatsava Naidu
- Center of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Prasad V Bharatam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
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Andrés CMC, Pérez de la Lastra JM, Munguira EB, Andrés Juan C, Pérez-Lebeña E. Dual-Action Therapeutics: DNA Alkylation and Antimicrobial Peptides for Cancer Therapy. Cancers (Basel) 2024; 16:3123. [PMID: 39335095 PMCID: PMC11429518 DOI: 10.3390/cancers16183123] [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: 08/02/2024] [Revised: 09/06/2024] [Accepted: 09/07/2024] [Indexed: 09/30/2024] Open
Abstract
Cancer remains one of the most difficult diseases to treat, requiring continuous research into innovative therapeutic strategies. Conventional treatments such as chemotherapy and radiotherapy are effective to a certain extent but often have significant side effects and carry the risk of resistance. In recent years, the concept of dual-acting therapeutics has attracted considerable attention, particularly the combination of DNA alkylating agents and antimicrobial peptides. DNA alkylation, a well-known mechanism in cancer therapy, involves the attachment of alkyl groups to DNA, leading to DNA damage and subsequent cell death. Antimicrobial peptides, on the other hand, have been shown to be effective anticancer agents due to their ability to selectively disrupt cancer cell membranes and modulate immune responses. This review aims to explore the synergistic potential of these two therapeutic modalities. It examines their mechanisms of action, current research findings, and the promise they offer to improve the efficacy and specificity of cancer treatments. By combining the cytotoxic power of DNA alkylation with the unique properties of antimicrobial peptides, dual-action therapeutics may offer a new and more effective approach to fighting cancer.
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Affiliation(s)
- Celia María Curieses Andrés
- Hospital Clínico Universitario de Valladolid, Avenida de Ramón y Cajal, 3, 47003 Valladolid, Spain; (C.M.C.A.); (E.B.M.)
| | - José Manuel Pérez de la Lastra
- Institute of Natural Products and Agrobiology, CSIC-Spanish Research Council, Avda. Astrofísico Fco. Sánchez, 3, 38206 La Laguna, Spain
| | - Elena Bustamante Munguira
- Hospital Clínico Universitario de Valladolid, Avenida de Ramón y Cajal, 3, 47003 Valladolid, Spain; (C.M.C.A.); (E.B.M.)
| | - Celia Andrés Juan
- Cinquima Institute and Department of Organic Chemistry, Faculty of Sciences, Valladolid University, Paseo de Belén, 7, 47011 Valladolid, Spain;
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Malešević A, Kolesárová M, Čartolovni A. Encompassing trust in medical AI from the perspective of medical students: a quantitative comparative study. BMC Med Ethics 2024; 25:94. [PMID: 39223538 PMCID: PMC11367737 DOI: 10.1186/s12910-024-01092-2] [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: 05/03/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND In the years to come, artificial intelligence will become an indispensable tool in medical practice. The digital transformation will undoubtedly affect today's medical students. This study focuses on trust from the perspective of three groups of medical students - students from Croatia, students from Slovakia, and international students studying in Slovakia. METHODS A paper-pen survey was conducted using a non-probabilistic convenience sample. In the second half of 2022, 1715 students were surveyed at five faculties in Croatia and three in Slovakia. RESULTS Specifically, 38.2% of students indicated familiarity with the concept of AI, while 44.8% believed they would use AI in the future. Patient readiness for the implementation of technologies was mostly assessed as being low. More than half of the students, 59.1%, believe that the implementation of digital technology (AI) will negatively impact the patient-physician relationship and 51,3% of students believe that patients will trust physicians less. The least agreement with the statement was observed among international students, while a higher agreement was expressed by Slovak and Croatian students 40.9% of Croatian students believe that users do not trust the healthcare system, 56.9% of Slovak students agree with this view, while only 17.3% of international students share this opinion. The ability to explain to patients how AI works if they were asked was statistically significantly different for the different student groups, international students expressed the lowest agreement, while the Slovak and Croatian students showed a higher agreement. CONCLUSION This study provides insight into medical students' attitudes from Croatia, Slovakia, and international students regarding the role of artificial intelligence (AI) in the future healthcare system, with a particular emphasis on the concept of trust. A notable difference was observed between the three groups of students, with international students differing from their Croatian and Slovak colleagues. This study also highlights the importance of integrating AI topics into the medical curriculum, taking into account national social & cultural specificities that could negatively impact AI implementation if not carefully addressed.
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Affiliation(s)
- Anamaria Malešević
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Zagreb, Croatia.
| | - Mária Kolesárová
- Institute of Social Medicine and Medical Ethics, School of Medicine, Comenius University in Bratislava, Bratislava, Slovakia
| | - Anto Čartolovni
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Zagreb, Croatia
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Ozcelik F, Dundar MS, Yildirim AB, Henehan G, Vicente O, Sánchez-Alcázar JA, Gokce N, Yildirim DT, Bingol NN, Karanfilska DP, Bertelli M, Pojskic L, Ercan M, Kellermayer M, Sahin IO, Greiner-Tollersrud OK, Tan B, Martin D, Marks R, Prakash S, Yakubi M, Beccari T, Lal R, Temel SG, Fournier I, Ergoren MC, Mechler A, Salzet M, Maffia M, Danalev D, Sun Q, Nei L, Matulis D, Tapaloaga D, Janecke A, Bown J, Cruz KS, Radecka I, Ozturk C, Nalbantoglu OU, Sag SO, Ko K, Arngrimsson R, Belo I, Akalin H, Dundar M. The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution. Funct Integr Genomics 2024; 24:138. [PMID: 39147901 DOI: 10.1007/s10142-024-01417-9] [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: 07/02/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.
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Affiliation(s)
- Firat Ozcelik
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Mehmet Sait Dundar
- Department of Electrical and Computer Engineering, Graduate School of Engineering and Sciences, Abdullah Gul University, Kayseri, Turkey
| | - A Baki Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Gary Henehan
- School of Food Science and Environmental Health, Technological University of Dublin, Dublin, Ireland
| | - Oscar Vicente
- Institute for the Conservation and Improvement of Valencian Agrodiversity (COMAV), Universitat Politècnica de València, Valencia, Spain
| | - José A Sánchez-Alcázar
- Centro de Investigación Biomédica en Red: Enfermedades Raras, Centro Andaluz de Biología del Desarrollo (CABD-CSIC-Universidad Pablo de Olavide), Instituto de Salud Carlos III, Sevilla, Spain
| | - Nuriye Gokce
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Duygu T Yildirim
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Nurdeniz Nalbant Bingol
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
| | - Dijana Plaseska Karanfilska
- Research Centre for Genetic Engineering and Biotechnology, Macedonian Academy of Sciences and Arts, Skopje, Macedonia
| | | | - Lejla Pojskic
- Institute for Genetic Engineering and Biotechnology, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Mehmet Ercan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Miklos Kellermayer
- Department of Biophysics and Radiation Biology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Izem Olcay Sahin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | | | - Busra Tan
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Donald Martin
- University Grenoble Alpes, CNRS, TIMC-IMAG/SyNaBi (UMR 5525), Grenoble, France
| | - Robert Marks
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Satya Prakash
- Department of Biomedical Engineering, University of McGill, Montreal, QC, Canada
| | - Mustafa Yakubi
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey
| | - Tommaso Beccari
- Department of Pharmeceutical Sciences, University of Perugia, Perugia, Italy
| | - Ratnesh Lal
- Neuroscience Research Institute, University of California, Santa Barbara, USA
| | - Sehime G Temel
- Department of Translational Medicine, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
- Department of Histology and Embryology, Faculty of Medicine, Bursa Uludag University, Bursa, Turkey
| | - Isabelle Fournier
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - M Cerkez Ergoren
- Department of Medical Genetics, Near East University Faculty of Medicine, Nicosia, Cyprus
| | - Adam Mechler
- Department of Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, Australia
| | - Michel Salzet
- Réponse Inflammatoire et Spectrométrie de Masse-PRISM, University of Lille, Lille, France
| | - Michele Maffia
- Department of Experimental Medicine, University of Salento, Via Lecce-Monteroni, Lecce, 73100, Italy
| | - Dancho Danalev
- University of Chemical Technology and Metallurgy, Sofia, Bulgaria
| | - Qun Sun
- Department of Food Science and Technology, Sichuan University, Chengdu, China
| | - Lembit Nei
- School of Engineering Tallinn University of Technology, Tartu College, Tartu, Estonia
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Dana Tapaloaga
- Faculty of Veterinary Medicine, University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andres Janecke
- Department of Paediatrics I, Medical University of Innsbruck, Innsbruck, Austria
- Division of Human Genetics, Medical University of Innsbruck, Innsbruck, Austria
| | - James Bown
- School of Science, Engineering and Technology, Abertay University, Dundee, UK
| | | | - Iza Radecka
- School of Science, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK
| | - Celal Ozturk
- Department of Software Engineering, Erciyes University, Kayseri, Turkey
| | - Ozkan Ufuk Nalbantoglu
- Department of Computer Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
| | - Sebnem Ozemri Sag
- Department of Medical Genetics, Bursa Uludag University Faculty of Medicine, Bursa, Turkey
| | - Kisung Ko
- Department of Medicine, College of Medicine, Chung-Ang University, Seoul, Korea
| | - Reynir Arngrimsson
- Iceland Landspitali University Hospital, University of Iceland, Reykjavik, Iceland
| | - Isabel Belo
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Hilal Akalin
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
| | - Munis Dundar
- Department of Medical Genetics, Faculty of Medicine, Erciyes University, Kayseri, Turkey.
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Jazieh AR, El Rouby N, Guinigundo A, Huelsman KM, Curran E, Khan R, Grund J, Calvo AR, Claes JJ, Overton SC, Hellard S, Vasiliadis L, Liu M, Blaxall BC. A Systematic Approach to Optimize the Implementation of Precision Oncology in Clinical Practice: A Meeting Proceeding. JOURNAL OF IMMUNOTHERAPY AND PRECISION ONCOLOGY 2024; 7:210-216. [PMID: 39219991 PMCID: PMC11361341 DOI: 10.36401/jipo-23-41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/25/2024] [Accepted: 02/27/2024] [Indexed: 09/04/2024]
Affiliation(s)
| | - Nihal El Rouby
- University of Cincinnati, St Elizabeth Hospital, Cincinnati, OH, USA
| | | | | | - Emily Curran
- Hematology Oncology Division, University of Cincinnati, Cincinnati, OH, USA
| | | | | | | | - Jason J. Claes
- TriHealth Cancer and Blood Institute, Cincinnati, OH, USA
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Chapla D, Chorya HP, Ishfaq L, Khan A, Vr S, Garg S. An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review. Cureus 2024; 16:e66688. [PMID: 39268329 PMCID: PMC11390952 DOI: 10.7759/cureus.66688] [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: 06/26/2024] [Accepted: 08/11/2024] [Indexed: 09/15/2024] Open
Abstract
Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.
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Affiliation(s)
- Deep Chapla
- Medicine, Jiangsu University, Zhenjiang, CHN
| | | | - Lyluma Ishfaq
- Medicine, Directorate of Health Services Kashmir, Srinagar, IND
| | - Afrasayab Khan
- Internal Medicine, Central Michigan University College of Medicine, Saginaw, USA
| | - Subrahmanyan Vr
- Internal Medicine Pediatrics, Armed Forces Medical College, Pune, IND
| | - Sheenam Garg
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
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41
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Tucci F, Laurinavicius A, Kather JN, Eloy C. The digital revolution in pathology: Towards a smarter approach to research and treatment. TUMORI JOURNAL 2024; 110:241-251. [PMID: 38606831 DOI: 10.1177/03008916241231035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.
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Affiliation(s)
- Francesco Tucci
- School of Pathology, University of Milan, Milan, Italy
- European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Arvydas Laurinavicius
- Department of Pathology, Forensic Medicine and Pharmacology, Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
- National Centre of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Catarina Eloy
- Ipatimup - Institute of Molecular Pathology and Immunology of University of Porto, Porto, Portugal
- Medical Faculty, University of Porto, Porto, Portugal
- i3S-Instituto de Investigação e Inovação em Saúde, Porto, Portugal
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42
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Ruprecht NA, Kennedy JD, Bansal B, Singhal S, Sens D, Maggio A, Doe V, Hawkins D, Campbel R, O’Connell K, Gill JS, Schaefer K, Singhal SK. Transcriptomics and epigenetic data integration learning module on Google Cloud. Brief Bioinform 2024; 25:bbae352. [PMID: 39101486 PMCID: PMC11299028 DOI: 10.1093/bib/bbae352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/12/2024] [Accepted: 07/06/2024] [Indexed: 08/06/2024] Open
Abstract
Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. HIGHLIGHTS
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Affiliation(s)
- Nathan A Ruprecht
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Joshua D Kennedy
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Chemistry and Physics, Drury University, 900 N. Benton Avenue, Springfield, MO 65802, United States
| | - Benu Bansal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sonalika Singhal
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Donald Sens
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Angela Maggio
- Deloitte, Health Data and AI, Deloitte Consulting LLP, 1919 N. Lynn Street, Suite 1500, Arlington, VA 22209, United States
| | - Valena Doe
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Dale Hawkins
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Ross Campbel
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Kyle O’Connell
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Jappreet Singh Gill
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Kalli Schaefer
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sandeep K Singhal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
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Arif WM. Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology. Int J Gen Med 2024; 17:3129-3136. [PMID: 39049835 PMCID: PMC11268710 DOI: 10.2147/ijgm.s465944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
Study Purpose This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.
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Affiliation(s)
- Wejdan M Arif
- King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia
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Harmantepe AT, Dulger UC, Gonullu E, Dikicier E, Şentürk A, Eröz E. A method for predicting mortality in acute mesenteric ischemia: Machine learning. ULUS TRAVMA ACIL CER 2024; 30:487-492. [PMID: 38967529 PMCID: PMC11331353 DOI: 10.14744/tjtes.2024.48074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/02/2024] [Accepted: 06/07/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND This study aimed to develop and validate an artificial intelligence model using machine learning (ML) to predict hospital mortality in patients with acute mesenteric ischemia (AMI). METHODS A total of 122 patients diagnosed with AMI at Sakarya University Training and Research Hospital between January 2011 and June 2023 were included in the study. These patients were divided into a training cohort (n=97) and a validation cohort (n=25), and further categorized as survivors and non-survivors during hospitalization. Serum-based laboratory results served as features. Hyperfeatures were eliminated using Recursive Feature Elimination (RFE) in Python to optimize outcomes. ML algorithms and data analyses were performed using Python (version 3.7). RESULTS Of the patients, 56.5% were male (n=69) and 43.5% were female (n=53). The mean age was 71.9 years (range 39-94 years). The mortality rate during hospitalization was 50% (n=61). To achieve optimal results, the model incorporated features such as age, red cell distribution width (RDW), C-reactive protein (CRP), D-dimer, lactate, globulin, and creatinine. Success rates in test data were as follows: logistic regression (LG), 80%; random forest (RF), 60%; k-nearest neighbor (KN), 52%; multilayer perceptron (MLP), 72%; and support vector classifier (SVC), 84%. A voting classifier (VC), aggregating votes from all models, achieved an 84% success rate. Among the models, SVC (sensitivity 1.0, specificity 0.77, area under the curve (AUC) 0.90, Confidence Interval (95%): (0.83-0.84)) and VC (sensitivity 1.0, specificity 0.77, AUC 0.88, Confidence Interval (95%): (0.83-0.84)) were noted for their effectiveness. CONCLUSION Independent risk factors for mortality were identified in patients with AMI. An efficient and rapid method using various ML models to predict mortality has been developed.
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Affiliation(s)
- Ahmet Tarık Harmantepe
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Ugur Can Dulger
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Emre Gonullu
- Department of Gastroenterology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Enis Dikicier
- Department of General Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Adem Şentürk
- Department of Oncology Surgery, Sakarya University Faculty of Medicine, Sakarya-Türkiye
| | - Erhan Eröz
- Department of General Surgery, Sakarya University Training and Research Hospital, Sakarya-Türkiye
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Shahrabani E, Shen M, Wuu YR, Potters L, Parashar B. Artificial Neural Network Prediction of Mortality in Cancer Patients Presenting for Radiation Therapy at a Multisite Institution. Cureus 2024; 16:e64536. [PMID: 39011317 PMCID: PMC11247042 DOI: 10.7759/cureus.64536] [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: 07/14/2024] [Indexed: 07/17/2024] Open
Abstract
INTRODUCTION For many decades, the management of cancer has utilized radiation therapy, which continues to evolve with technology to improve patient outcomes. However, despite the standardization of treatment plans and the establishment of best clinical practices based on prospective, randomized trials and adherence to National Comprehensive Cancer Network (NCCN) guidelines, the outcomes from radiation therapy are highly variable and dependent on a number of factors, including patient demographics, tumor characteristics/histology, and treatment parameters. In this study, we attempt to use available patient data and treatment parameters at the time of radiation therapy to predict future outcomes using artificial intelligence (AI). METHODS Six thousand five hundred ninety-five cases of patients who completed radiation treatment were selected retrospectively and used to train artificial neural networks (ANNs) and baseline models (i.e., logistic regression, random forest, support vector machines [SVMs], gradient boosting [XGBoost]) for binary classification of mortality at multiple time points ranging from six months to five years post-treatment. A hyperparameter grid search was used to identify the optimal network architecture for each time point, using sensitivity as the primary outcome metric. RESULTS The median age was 75 years (range: 2-102 years). There were 63.8% females and 36.1% males. The results indicate that ANNs were able to successfully perform binary mortality prediction with an accuracy greater than random chance and greater sensitivity than baseline models used. The best-performing algorithm was the ANN, which achieved a sensitivity of 83.00% ± 4.89% for five-year mortality. CONCLUSION The neural network was able to achieve higher sensitivity than Logistic Regression, SVM Random Forest, and XGBoost across all output target variables, demonstrating the utility of a neural network model for mortality prediction on the provided dataset.
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Affiliation(s)
- Elan Shahrabani
- Department of Radiation Oncology, Northwell/Donald and Barbara Zucker School of Medicine at Hofstra, New Hyde Park, USA
| | - Michael Shen
- Department of Radiation Oncology, Northwell/Donald and Barbara Zucker School of Medicine at Hofstra, New Hyde Park, USA
| | - Yen-Ruh Wuu
- Department of Radiation Oncology, Northwell/Donald and Barbara Zucker School of Medicine at Hofstra, New Hyde Park, USA
| | - Louis Potters
- Department of Radiation Oncology, Northwell/Donald and Barbara Zucker School of Medicine at Hofstra, New Hyde Park, USA
| | - Bhupesh Parashar
- Department of Radiation Oncology, Northwell/Donald and Barbara Zucker School of Medicine at Hofstra, New Hyde Park, USA
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Shelton WJ, Zandpazandi S, Nix JS, Gokden M, Bauer M, Ryan KR, Wardell CP, Vaske OM, Rodriguez A. Long-read sequencing for brain tumors. Front Oncol 2024; 14:1395985. [PMID: 38915364 PMCID: PMC11194609 DOI: 10.3389/fonc.2024.1395985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.
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Affiliation(s)
- William J Shelton
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Sara Zandpazandi
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States
| | - J Stephen Nix
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Murat Gokden
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Michael Bauer
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Katie Rose Ryan
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Christopher P Wardell
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Olena Morozova Vaske
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, United States
| | - Analiz Rodriguez
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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Patel R, Masys T, Baridi R. Exploring the Impact of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Esthesioneuroblastomas: A Comprehensive Review. Cureus 2024; 16:e62683. [PMID: 39036183 PMCID: PMC11258942 DOI: 10.7759/cureus.62683] [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: 06/19/2024] [Indexed: 07/23/2024] Open
Abstract
Esthesioneuroblastomas (ENBs) present unique diagnostic and therapeutic challenges due to their rare and complex clinical presentation. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as promising tools in various medical specialties, revolutionizing diagnostic accuracy, treatment planning, and patient outcomes. However, their application in ENBs remains relatively unexplored. This comprehensive literature review aims to evaluate the current state of AI and ML technologies in ENB diagnosis, radiological and histopathological imaging, and treatment planning. By synthesizing existing evidence and identifying gaps in knowledge, this review aims to showcase the potential benefits, limitations, and future directions of integrating AI and ML into the multidisciplinary management of ENBs.
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Affiliation(s)
- Raj Patel
- Otolaryngology - Head and Neck Surgery, Loyola University Chicago Stritch School of Medicine, Chicago, USA
| | - Tadas Masys
- Medicine, Loyola University Chicago Stritch School of Medicine, Chicago, USA
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Preetam S, Mondal S, Priya S, Bora J, Ramniwas S, Rustagi S, Qusty NF, Alghamdi S, Babalghith AO, Siddiqi A, Malik S. Targeting tumour markers in ovarian cancer treatment. Clin Chim Acta 2024; 559:119687. [PMID: 38663473 DOI: 10.1016/j.cca.2024.119687] [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: 03/10/2024] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
Ovarian cancers (OC) are the most common, lethal, and stage-dependent cancers at the global level, specifically in female patients. Targeted therapies involve the administration of drugs that specifically target the alterations in tumour cells responsible for their growth, proliferation, and metastasis, with the aim of treating particular patients. Presently, within the realm of gynaecological malignancies, specifically in breast and OCs, there exist various prospective therapeutic targets encompassing tumour-intrinsic signalling pathways, angiogenesis, homologous-recombination deficit, hormone receptors, and immunologic components. Breast cancers are often detected in advanced stages, primarily due to the lack of a reliable screening method. However, various tumour markers have been extensively researched and employed to evaluate the condition, progression, and effectiveness of medication treatments for this ailment. The emergence of recent technological advancements in the domains of bioinformatics, genomics, proteomics, and metabolomics has facilitated the exploration and identification of hitherto unknown biomarkers. The primary objective of this comprehensive review is to meticulously investigate and analyze both established and emerging methodologies employed in the identification of tumour markers associated with OC.
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Affiliation(s)
- Subham Preetam
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST) Dalseong-gun, Daegu 42988, South Korea.
| | - Sagar Mondal
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Swati Priya
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Jutishna Bora
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
| | - Seema Ramniwas
- University Center for Research and Development, Department of Biotechnology, Chandigarh University, Gharuan, Mohali 140413, India.
| | - Sarvesh Rustagi
- School of Applied and Life Sciences, Uttaranchal University, 248007 Dehradun, Uttarakhand, India.
| | - Naeem F Qusty
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Saad Alghamdi
- Department of Clinical Laboratory Sciences, Faculty of Applied Medical Sciences, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Ahmad O Babalghith
- Medical Genetics Department, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Abdullah Siddiqi
- Department of Clinical Laboratory, Makkah Park Clinics, Makkah, Saudi Arabia.
| | - Sumira Malik
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, Jharkhand 834001, India.
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Lynch SM, Heeran AB, Burke C, Lynam-Lennon N, Eustace AJ, Dean K, Robson T, Rahman A, Marcone S. Precision Oncology, Artificial Intelligence, and Novel Therapeutic Advancements in the Diagnosis, Prevention, and Treatment of Cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference. Cancers (Basel) 2024; 16:1989. [PMID: 38893110 PMCID: PMC11171401 DOI: 10.3390/cancers16111989] [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: 04/19/2024] [Revised: 05/21/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Advancements in oncology, especially with the era of precision oncology, is resulting in a paradigm shift in cancer care. Indeed, innovative technologies, such as artificial intelligence, are paving the way towards enhanced diagnosis, prevention, and personalised treatments as well as novel drug discoveries. Despite excellent progress, the emergence of resistant cancers has curtailed both the pace and extent to which we can advance. By combining both their understanding of the fundamental biological mechanisms and technological advancements such as artificial intelligence and data science, cancer researchers are now beginning to address this. Together, this will revolutionise cancer care, by enhancing molecular interventions that may aid cancer prevention, inform clinical decision making, and accelerate the development of novel therapeutic drugs. Here, we will discuss the advances and approaches in both artificial intelligence and precision oncology, presented at the 59th Irish Association for Cancer Research annual conference.
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Affiliation(s)
- Seodhna M. Lynch
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry BT47 6SB, UK;
| | - Aisling B. Heeran
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, D04 C1P1 Dublin, Ireland;
| | - Niamh Lynam-Lennon
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
| | - Alex J. Eustace
- Life Sciences Institute, Dublin City University, D09 NR58 Dublin, Ireland;
| | - Kellie Dean
- School of Biochemistry and Cell Biology, Western Gateway Building, University College Cork, T12 XF62 Cork, Ireland;
| | - Tracy Robson
- School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, D02 YN77 Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Belfield, D04 C1P1 Dublin, Ireland;
| | - Simone Marcone
- Department of Surgery, Trinity Translational Medicine Institute, Trinity St. James’s Cancer Institute, Trinity College Dublin, D02 PN40 Dublin, Ireland; (A.B.H.); (N.L.-L.); (S.M.)
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Peng H, Su M, Guo X, Shi L, Lei T, Yu H, Xu J, Pan X, Chen X. Artificial intelligence-based prognostic model accurately predicts the survival of patients with diffuse large B-cell lymphomas: analysis of a large cohort in China. BMC Cancer 2024; 24:621. [PMID: 38773392 PMCID: PMC11110380 DOI: 10.1186/s12885-024-12337-z] [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: 11/05/2023] [Accepted: 05/03/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include molecular data. Therefore, we developed a widely applicable novel scoring system with molecular indicators screened by artificial intelligence (AI) that achieves accurate prognostic stratification and promotes individualized treatments. METHODS We retrospectively enrolled a cohort of 401 patients with DLBCL from our hospital, covering the period from January 2015 to January 2019. We included 22 variables in our analysis and assigned them weights using the random survival forest method to establish a new predictive model combining bidirectional long-short term memory (Bi-LSTM) and logistic hazard techniques. We compared the predictive performance of our "molecular-contained prognostic model" (McPM) and the IPI. In addition, we developed a simplified version of the McPM (sMcPM) to enhance its practical applicability in clinical settings. We also demonstrated the improved risk stratification capabilities of the sMcPM. RESULTS Our McPM showed superior predictive accuracy, as indicated by its high C-index and low integrated Brier score (IBS), for both overall survival (OS) and progression-free survival (PFS). The overall performance of the McPM was also better than that of the IPI based on receiver operating characteristic (ROC) curve fitting. We selected five key indicators, including extranodal involvement sites, lactate dehydrogenase (LDH), MYC gene status, absolute monocyte count (AMC), and platelet count (PLT) to establish the sMcPM, which is more suitable for clinical applications. The sMcPM showed similar OS results (P < 0.0001 for both) to the IPI and significantly better PFS stratification results (P < 0.0001 for sMcPM vs. P = 0.44 for IPI). CONCLUSIONS Our new McPM, including both clinical and molecular variables, showed superior overall stratification performance to the IPI, rendering it more suitable for the molecular era. Moreover, our sMcPM may become a widely used and effective stratification tool to guide individual precision treatments and drive new drug development.
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Affiliation(s)
- Huilin Peng
- Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Mengmeng Su
- Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, 310053, China
| | - Xiang Guo
- Zhejiang University of Science & Technology, Hangzhou, Zhejiang, 310027, China
| | - Liang Shi
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Tao Lei
- Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Haifeng Yu
- Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Jieyu Xu
- Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Xiaohua Pan
- Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, 310053, China.
| | - Xi Chen
- Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
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