An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects

摘要: Abstract Mathematical modeling of cerebral tumor growth is great importance in clinics. It can help understanding the physiology growth, future prognosis shape and volume, quantify aggressiveness, response to therapy. This be achieved at macroscopic level using medical imaging techniques (particularly, magnetic resonance (MRI) diffusion tensor (DTI)) complex mathematical models which are either diffusive or biomechanical. We propose an optimized generic framework that couples diffusivity infiltration with induced mass effect. Tumor cell captured a modified reaction-diffusion model logistic proliferation term. On other hand, effect modeled continuum mechanics formulation. In addition, we consider treatment effects both radiotherapy chemotherapy. The efficacy chemotherapy included via adaptively log-kill method tissue heterogeneity while considered linear quadratic model. Moreover, our efficiently utilizes imaging. Furthermore, optimize parameters patient-specific bio-inspired particle swarm optimization (PSO) algorithm. Our tested on atlas real MRI scans 8 low grade gliomas subjects. Experimental results show incorporates modelingprocess. simulated growths have high accuracy terms Dice coefficient (average 87.1%) Jaccard index (77.14%) when compared follow up (ground truth) as well.