Program Information
Patient-Specific Mathematical Neuro-Oncology: Biologically-Informed Radiation Therapy and Imaging Physics
K Swanson1*, D Corwin2 , R Rockne3 , (1) Northwestern University, Chicago, IL, (2) Northwestern University, Chicago, IL, (3) ,
Presentations
WE-E-17A-7 Wednesday 1:45PM - 3:45PM Room: 17APurpose:
To demonstrate a method of generating patient-specific, biologically-guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2-MRI based response to radiation therapy using an MRI simulator.
Methods:
We have integrated a patient-specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated RT optimization to construct individualized, biologically-guided plans. Patient-individualized simulations of the standard-of-care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate the role of angiogenesis and its correlates in glioma response to therapy with the Proliferation-Invasion-Hypoxia- Necrosis-Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor-induced vasogenic edema, tumor and tissue density that is used in a multi-compartmental MRI signal equation for generation of simulated T2-weighted MRIs.
Results:
Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2-MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue.
Conclusion:
Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically-guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2-MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging-based response to RT.
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