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A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility
Alexandros Roniotis1, Mariam-Eleni Oraiopoulou2, Eleftheria Tzamali1
1Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece.
Cancer Informatics
|June 19, 2015
Summary
This study models glioblastoma growth using patient imaging data to predict tumor evolution. The model integrates cell proliferation, invasion, and vasculature, offering insights for optimizing treatment strategies.
Area of Science:
- Neuro-oncology
- Computational biology
- Medical imaging
Background:
- Glioblastoma multiforme is an aggressive brain tumor.
- Predicting glioma growth is crucial for treatment planning.
- Existing models are being advanced with new factors like vasculature and oxygenation.
Purpose of the Study:
- To initialize a glioma growth model using patient-specific imaging data.
- To present a feasibility study on predicting vasculature changes over time.
- To analyze the impact of specific parameters on simulated tumor growth patterns.
Main Methods:
- Utilizing a simulation model incorporating cell proliferation, invasion, angiogenic rates, oxygen consumption, and vasculature.
- Employing pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with Toft's model.
- Using K (trans) from DCE-MRI to quantify vasculature density and differentiate tumor regions.
Main Results:
- Demonstrated feasibility of initializing a glioma growth model with patient imaging data.
- Presented a study on the temporal prediction of vasculature.
- Showcased the effect of parameter variations on simulated glioblastoma growth patterns.
Conclusions:
- The developed model shows promise for initializing and simulating glioblastoma growth.
- Patient-specific imaging data and pharmacokinetic parameters are valuable inputs for tumor modeling.
- This approach aids in understanding tumor heterogeneity and optimizing therapeutic decisions.
