ArticleMedical image analysis2015
Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusion.
Article in Medical image analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026Article
- Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network.Diagnostics (Basel, Switzerland) · 2022Article
- Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2021Article
- Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.Nature protocols · 2021Review
- Article
- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2021Article
- Towards integration ofScientific reports · 2020Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- Coupling brain-tumor biophysical models and diffeomorphic image registration.Computer methods in applied mechanics and engineering · 2019Article
- Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.IEEE transactions on medical imaging · 2018Article
- A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predictJournal of the Royal Society, Interface · 2017Article
- Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Pancreatic Tumor Growth Prediction With Elastic-Growth Decomposition, Image-Derived Motion, and FDM-FEM Coupling.IEEE transactions on medical imaging · 2017Article
- Progress in Fully Automated Abdominal CT Interpretation.AJR. American journal of roentgenology · 2016Review
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Authors and funding
4 authors.
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Abstract
The goal of tumor growth prediction is to model the tumor growth process, which can be achieved by physiological modeling and model personalization from clinical measurements. Although image-driven frameworks have been proposed with promising results, several issues such as infinitesimal strain assumptions, complicated personalization procedures, and the lack of functional information, may limit their prediction accuracy. In view of these issues, we propose a framework for pancreatic neuroendocrine tumor growth prediction, which comprises a FEM-based tumor growth model with coupled reaction-diffusion equation and nonlinear biomechanics. Physiological data fusion of structural and functional images is used to improve the subject-specificity of model personalization, and a derivative-free global optimization algorithm is adopted to facilitate the complicated model and accommodate flexible choices of objective functions. With this flexibility, we propose an objective function accounting for both the tumor volume difference and the root-mean-squared error of intracellular volume fractions. Experiments were performed on synthetic and clinical data to verify the parameter estimation capability and the prediction performance. Comparisons of using different biomechanical models and objective functions were also performed. From the experimental results of eight patient data sets, the average recall, precision, Dice coefficient, and relative volume difference between predicted and measured tumor volumes were 84.5 ± 6.9%, 85.8 ± 8.2%, 84.6 ± 1.7%, and 14.2 ± 8.4%, respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.