TY  - EJOU
AU  - Faizal, Wan Mohd 
AU  - Mazlan, Nurul Musfirah 
AU  - Shaukat, Shazril Imran 
AU  - Khor, Chu Yee 
AU  - Haidiezul, Ab Hadi Mohd 
AU  - Syafiq, Abdul Khadir Mohamad 

TI  - The Convergence of Computational Fluid Dynamics and Machine Learning in Oncology: A Review
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 2
SN  - 1526-1506

AB  - 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 enables patient-specific treatment planning. Methodologically, it covers multi-scale modeling approaches, nanotherapeutic simulations, imaging integration, and emerging AI-driven frameworks. The paper identifies gaps in current applications, including the need for robust clinical validation, real-time model adaptability, and ethical data integration. Future directions suggest that CFD–ML hybrids could serve as digital twins for tumor evolution, offering insights for adaptive therapies. The review advocates for a computationally augmented oncology ecosystem that combines biological complexity with engineering precision for next-generation cancer care.
KW  - Computational fluid dynamics (CFD); machine learning (ML); cancer modeling; drug delivery simulation; tumor microenvironment

DO  - 10.32604/cmes.2025.068660