Choroid neovascularization (CNV) is caused by new blood vessels growing in the choroid and penetrating the bruch membrane. It is the major cause of vision disability in many retinal diseases. Though anti-vascular endothelial growth factor injection has proved to be effective for treating CNV, treatment planning is essential to ensure the efficacy while reducing the risk. For this purpose, we propose a CNV growth model based on longitudinal optical coherence tomography (OCT) images. The reaction-diffusion model is applied to simulate the growth and shrinkage of CNV volumes, and is solved by using the finite-element method. A fitted curve of the CNV growth/shrinkage rate is obtained by optimizing the growth parameters. Then, the trained parameters are applied to the predicted image to get the simulated image, which is compared with the validated image to evaluate the accuracy of prediction. The proposed method was tested on a dataset with seven patients in which each patient has 12 longitudinal OCT images. The resulted mean dice coefficient is 76.40% ± 8.20%. The experimental results show a promising step towards the image-guided patient-specific treatment.