Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusion

•Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free global optimization algorithm for model parameter estimation.•Physiological data fusion of contrast-enhanced CT and FDG-PET images.•Average pr...

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Vydané v:Medical image analysis Ročník 25; číslo 1; s. 72 - 85
Hlavní autori: Wong, Ken C.L., Summers, Ronald M., Kebebew, Electron, Yao, Jianhua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier B.V 01.10.2015
Predmet:
Algorithms
Biomechanical Phenomena
Contrast Media
Derivative-free optimization
Fluorodeoxyglucose F18
Humans
Model personalization
Neoplasms - pathology
Nonlinear solid mechanics
Physiological data fusion
Positron-Emission Tomography - methods
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted - methods
Radiopharmaceuticals
Sensitivity and Specificity
Tomography, X-Ray Computed - methods
Tumor growth prediction
Nonlinear solid mechanics
Model personalization
Tumor growth prediction
Physiological data fusion
Derivative-free optimization
ISSN:1361-8415, 1361-8423
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Shrnutí:•Tumor growth prediction with physiological data fusion.•A tumor growth model with reaction-diffusion and hyperelastic biomechanical model.•A derivative-free global optimization algorithm for model parameter estimation.•Physiological data fusion of contrast-enhanced CT and FDG-PET images.•Average prediction performance: Dice = 84.6%, relative volume difference = 14.2%. [Display omitted] 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2015.04.002