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Open AccessArticle
Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment
by
Anwar Shams
Anwar Shams 1,2,3
1
Department of Pharmacology, College of Medicine, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
2
Research Center for Health Sciences, Deanship of Graduate Studies and Scientific Research, Taif University, Taif 26432, Saudi Arabia
3
High Altitude Research Center, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Diagnostics 2024, 14(19), 2174; https://doi.org/10.3390/diagnostics14192174 (registering DOI)
Submission received: 7 August 2024
/
Revised: 17 September 2024
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Accepted: 23 September 2024
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Published: 29 September 2024
Abstract
Background: Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient’s unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options. Methodology: We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models’ recent innovations and implementations to aid in molecular diagnosis and treatment planning. Results: The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target’s likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs. Conclusions: Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient’s demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.
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MDPI and ACS Style
Shams, A.
Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment. Diagnostics 2024, 14, 2174.
https://doi.org/10.3390/diagnostics14192174
AMA Style
Shams A.
Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment. Diagnostics. 2024; 14(19):2174.
https://doi.org/10.3390/diagnostics14192174
Chicago/Turabian Style
Shams, Anwar.
2024. "Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment" Diagnostics 14, no. 19: 2174.
https://doi.org/10.3390/diagnostics14192174
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