Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review

Area of Science:

• Oncology
• Computational Biology
• Artificial Intelligence

Background:

• Solid tumors arise from uncontrolled cell division and microenvironmental interactions, impacting growth and therapeutic resistance.
• Current anti-cancer drugs often cause off-target toxicity, highlighting the need for precise, personalized detection and treatment strategies.
• The rising global cancer burden necessitates advanced diagnostic and therapeutic approaches.

Purpose of the Study:

• To analyze breakthroughs in artificial intelligence (AI), hybrid frameworks, and mathematical modeling (MM) for tumor diagnosis and treatment.
• To examine challenges associated with data-driven methodologies in oncology.
• To highlight emerging trends and provide future recommendations for AI and MM in cancer care.

Main Methods:

• Review of recent advancements in AI techniques (machine learning, deep learning) for cancer diagnosis, surgical planning, and outcome prediction.
• Application of mathematical models to understand tumor growth dynamics and optimize therapeutic interventions.
• Exploration of hybrid frameworks combining AI and MM for enhanced cancer management.

Main Results:

• AI and MM significantly enhance cancer detection, prediction, and treatment efficacy through precise, optimized hybrid techniques.
• Computational methods offer insights into tumor behavior and aid in simulating treatment responses.
• Key challenges include data scarcity, model interpretability, and generalization issues, requiring robust and explainable AI/MM solutions.

Conclusions:

• AI and MM show significant promise in neurosurgical oncology, offering potential for improved patient outcomes.
• Limitations such as data quality and model transparency must be addressed for widespread clinical adoption.
• Future research should focus on developing safer, more transparent, and individualized cancer care solutions using AI and MM.