Olawade, David B., Teke, Jennifer, Adeleye, Khadijat K., Egbon, Eghosasere, Weerasinghe, Kusal, Ovsepian, Saak V., Boussios, Stergios (2024) AI-guided cancer therapy for patients with coexisting migraines. Cancers, 16 (21). Article Number 3690. ISSN 2072-6694. (doi:10.3390/cancers16213690) (KAR id:107753)
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Official URL: https://doi.org/10.3390/cancers16213690 |
Abstract
Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.
Item Type: | Article |
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DOI/Identification number: | 10.3390/cancers16213690 |
Uncontrolled keywords: | anticancer therapy; migraine; machine learning; patient profiling; predictive modeling; artificial intelligence; personalized medicine |
Subjects: | R Medicine |
Divisions: | Divisions > Division of Natural Sciences > Kent and Medway Medical School |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
SWORD Depositor: | JISC Publications Router |
Depositing User: | JISC Publications Router |
Date Deposited: | 08 Nov 2024 11:34 UTC |
Last Modified: | 12 Nov 2024 09:47 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107753 (The current URI for this page, for reference purposes) |
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