Optimizing Radiotherapy for Glioblastoma Using A Patient-Specific Mathematical Model
D Corwin1*, C Holdsworth2, R Rockne1, R Stewart3, M Phillips3, K Swanson1, (1) Northwestern University, Chicago, IL, (2) Brigham and Women's Hospital, Boston, MA, (3) University of Washington, Seattle, WASU-E-T-295 Sunday 3:00PM - 6:00PM Room: Exhibit Hall
Purpose: To generate adaptive, biologically optimized, patient₋specific IMRT plans with the potential to reduce normal tissue complications and increase treatment efficacy in the treatment of glioblastoma.
Methods: A proliferation₋invasion radiation therapy (PIRT) mathematical model of glioblastoma characterizes patient₋specific tumor evolution and response to radiotherapy. An iterative dialog between the PIRT model and a multi₋objective evolutionary algorithm (MOEA) for IMRT plan generation results in adaptive, patient₋specific plans that can be optimized to clinical goals subject to defined restrictions. We performed simulations in a simplified geometry utilizing both the standard₋of₋care and optimized plans for a cohort of 11 patients exhibiting a wide range of tumor growth kinetics and compared the results.
Results: The spatially non₋uniform, patient₋specific optimized plans reduced equivalent uniform dose (EUD) to healthy brain tissue (39 ₋ 82%) and increased therapeutic ratio (the ratio of tumor EUD to normal tissue EUD) (50 ₋ 265%). The model₋driven virtual evaluation of cancer treatment response (VECTR) score, a metric of treatment impact on survival, increased for all but one patient (8 ₋ 181%). Both the normal tissue EUD and therapeutic ratio were linearly correlated with patient₋specific PIRT model parameters, indicating increased benefits or patients with more diffuse tumors. These results were robust to uncertainty in measured tumor radius of ± .5 mm and a 20% variation in the linear quadratic radiobiology parameter α/β.
Conclusion: This analysis suggests that we can improve upon the standard₋of₋care radiation therapy with adaptive, individualized plans generated with a patient₋specific mathematical model of glioblastoma in combination with a MOEA for IMRT optimization. This work demonstrates a possible improvement of patient outcomes and lays the groundwork for further 3D anatomically accurate simulations that further optimize treatment and spare eloquent brain.
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