Wan Mohd Faizal1,2,*, Nurul Musfirah Mazlan1,*, Shazril Imran Shaukat3,4, Chu Yee Khor2, Ab Hadi Mohd Haidiezul2, Abdul Khadir Mohamad Syafiq2
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1335-1369, 2025, DOI:10.32604/cmes.2025.068660
- 31 August 2025
Abstract Conventional oncology faces challenges such as suboptimal drug delivery, tumor heterogeneity, and therapeutic resistance, indicating a need for more personalized, and mechanistically grounded and predictive treatment strategies. This review explores the convergence of Computational Fluid Dynamics (CFD) and Machine Learning (ML) as an integrated framework to address these issues in modern cancer therapy. The paper discusses recent advancements where CFD models simulate complex tumor microenvironmental conditions, like interstitial fluid pressure (IFP) and drug perfusion, and ML enhances simulation workflows, automates image-based segmentation, and enhances predictive accuracy. The synergy between CFD and ML improves scalability and More >