Tumor growth prediction with hyperelastic biomechanical model, physiological data fusion, and nonlinear optimization

Tumor growth prediction is usually achieved by physiological modeling and model personalization from clinical measurements. Although image-based frameworks have been proposed with promising results, different issues such as infinitesimal strain assumption, complicated optimization procedures, and lack of functional information, may limit the prediction performance. Therefore, we propose a framework which comprises a hyperelastic biomechanical model for better physiological plausibility, gradient-free nonlinear optimization for more flexible choices of models and objective functions, and physiological data fusion of structural and functional images for better subject-specificity. Experiments were performed on synthetic and clinical data to verify parameter estimation capability and prediction performance of the framework. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results on eight patient data sets, the recall, precision, and relative volume difference (RVD) between predicted and measured tumor volumes are 84.85 ± 6.15%, 87.08 ± 7.83%, and 13.81 ± 6.64% respectively.