1
|
Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med 2024; 22:411. [PMID: 38702711 PMCID: PMC11069149 DOI: 10.1186/s12967-024-05067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 05/06/2024] Open
Abstract
Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.
Collapse
Affiliation(s)
- Claudio Carini
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, New Hunt's House, King's College London, Guy's Campus, London, UK.
- Biomarkers Consortium, Foundation of the National Institute of Health, Bethesda, MD, USA.
| | - Attila A Seyhan
- Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Joint Program in Cancer Biology, Lifespan Health System and Brown University, Providence, RI, USA.
- Legorreta Cancer Center at Brown University, Providence, RI, USA.
| |
Collapse
|
2
|
Derbal Y. Adaptive Control of Tumor Growth. Cancer Control 2024; 31:10732748241230869. [PMID: 38294947 PMCID: PMC10832444 DOI: 10.1177/10732748241230869] [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: 09/25/2023] [Revised: 12/04/2023] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
Cancer treatment optimizations select the most optimum combinations of drugs, sequencing schedules, and appropriate doses that would limit toxicity and yield an improved patient quality of life. However, these optimizations often lack an adequate consideration of cancer's near-infinite potential for evolutionary adaptation to therapeutic interventions. Adapting cancer therapy based on monitored tumor burden and clonal composition is an intuitively sound approach to the treatment of cancer as an inherently complex and adaptive system. The adaptation would be driven by clinical outcome setpoints embodying the aims to thwart therapeutic resistance and maintain a long-term management of the disease or even a cure. However, given the nonlinear, stochastic dynamics of tumor response to therapeutic interventions, adaptive therapeutic strategies may at least need a one-step-ahead prediction of tumor burden to maintain their control over tumor growth dynamics. The article explores the feasibility of adaptive cancer treatment driven by tumor state feedback assuming cell adaptive fitness to be the underlying source of phenotypic plasticity and pathway entropy as a biomarker of tumor growth trajectory. The exploration is undertaken using deterministic and stochastic models of tumor growth dynamics.
Collapse
Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada
| |
Collapse
|
3
|
Derbal Y. Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence. Cancer Control 2024; 31:10732748241264704. [PMID: 38897721 PMCID: PMC11189021 DOI: 10.1177/10732748241264704] [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: 04/04/2024] [Revised: 05/17/2024] [Accepted: 06/06/2024] [Indexed: 06/21/2024] Open
Abstract
Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.
Collapse
Affiliation(s)
- Youcef Derbal
- Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada
| |
Collapse
|
4
|
Bhattacharya S, Mahato RK, Singh S, Bhatti GK, Mastana SS, Bhatti JS. Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches. Life Sci 2023; 332:122110. [PMID: 37734434 DOI: 10.1016/j.lfs.2023.122110] [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: 07/06/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023]
Abstract
Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.
Collapse
Affiliation(s)
- Srinjan Bhattacharya
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Rahul Kumar Mahato
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India
| | - Satwinder Singh
- Department of Computer Science and Technology, Central University of Punjab, Bathinda 151401, Punjab, India.
| | - Gurjit Kaur Bhatti
- Department of Medical Lab Technology, University Institute of Applied Health Sciences, Chandigarh University, Mohali, India
| | - Sarabjit Singh Mastana
- School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Leicestershire, Loughborough LE11 3TU, UK.
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda 151401, Punjab, India.
| |
Collapse
|
5
|
McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [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: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
Collapse
Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
| |
Collapse
|
6
|
Sussman L, Garcia-Robledo JE, Ordóñez-Reyes C, Forero Y, Mosquera AF, Ruíz-Patiño A, Chamorro DF, Cardona AF. Integration of artificial intelligence and precision oncology in Latin America. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:1007822. [PMID: 36311461 PMCID: PMC9608820 DOI: 10.3389/fmedt.2022.1007822] [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: 07/31/2022] [Accepted: 09/21/2022] [Indexed: 11/07/2022] Open
Abstract
Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.
Collapse
Affiliation(s)
- Liliana Sussman
- Department of Neurology, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia,Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia
| | - Juan Esteban Garcia-Robledo
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,Division of Hematology/Oncology, Mayo Clinic, Scottsdale, AZ, United States
| | - Camila Ordóñez-Reyes
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Yency Forero
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Mosquera
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Alejandro Ruíz-Patiño
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Diego F. Chamorro
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia
| | - Andrés F. Cardona
- Foundation for Clinical and Applied Cancer Research – FICMAC, Bogotá, Colombia,MolecularOncology and Biology Systems Research Group (Fox-G), Universidad el Bosque, Bogotá, Colombia,Direction of Research, Science and Education, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (CTIC), Bogotá, Colombia,Correspondence: Andrés F. Cardona
| |
Collapse
|