SLIIT International Conference on Engineering and Technology [SICET]

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SLIIT International Conference on Engineering and Technology is organized by the Faculty of Engineering. SICET welcomes submissions from various disciplines, focusing on emerging trends in Engineering, Technology, and Applied and Natural Sciences. The conference will encompass research in theory, practical applications, and education. This event offers a unique platform for academics, student researchers, and industry practitioners to present innovative ideas and engage with professionals from diverse engineering fields

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Subject: search.filters.subject.Cancer

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    Predictive Modeling for Personalized Cancer Therapy Using Reinforcement Learning
    (Faculty of Engineering, 2025-09-09) Edirisinghe M.M; Gunarathne,J H M S M
    Adaptive therapy is transforming cancer treatment by enabling dynamic, patient-specific interventions that adapt to tumor progression and individual variability. Unlike traditional fixed-dose regimens, adaptive therapy leverages the evolutionary dynamics of tumors to extend treatment effectiveness and delay resistance. Reinforcement Learning (RL), an area of artificial intelligence focused on sequential decision-making, offers a robust framework for optimizing these adaptive strategies. RL can learn optimal treatment policies by interacting with computational models of tumor growth and drug response, continuously adjusting regimens based on observed tumor states, resistant cell populations, and biomarkers. This approach allows for the creation of personalized therapies that maintain long-term tumor control while minimizing toxicity and the emergence of resistance. The integration of RL into predictive modeling for cancer therapy represents a paradigm shift, enabling smarter, safer, and more effective treatments that are dynamically tailored to each patient’s evolving disease. This paper reviews the foundational concepts of adaptive therapy and RL discusses tumor modeling approaches, examines RL algorithms, and addresses current challenges and future directions in the field.