AI-Guided Cancer Therapy for Patients with Coexisting Migraines

Olawade, David B., Teke, Jennifer, Adeleye, Khadijat K. ORCID: https://orcid.org/0000-0001-7560-5504, Egbon, Eghosasere, Weerasinghe, Kusal, Ovsepian, Saak V. ORCID: https://orcid.org/0000-0002-9522-4159 and Boussios, Stergios ORCID: https://orcid.org/0000-0002-2512-6131 (2024) AI-Guided Cancer Therapy for Patients with Coexisting Migraines. Cancers, 16 (21).

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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.