Program Information
Optimizing Global Liver Function in Liver SBRT Treatment Planning
V Wu*, M Epelman , E Romeijn , M Feng , Y Cao , H Wang , R Ten Haken , M Matuszak , The University of Michigan, Ann Arbor, MI
Presentations
TH-AB-BRB-8 (Thursday, July 16, 2015) 7:30 AM - 9:30 AM Room: Ballroom B
Purpose: Liver SBRT patients have variable pre-treatment liver function, which strongly influences liver toxicity risk. Previous studies include a “perfusion-weighted” mean dose model as a surrogate for liver function in plan optimization. This work investigates the benefit of using a more sophisticated dose-response model that incorporates two important dose thresholds: (i) based on pre-treatment function, a significant low-dose may need to be exceeded before noticeable damage is done, and (ii) a high-dose saturation point, beyond which no additional damage is done.
Methods: Voxel-based liver perfusion from DCE-MRI was obtained. Two optimization models subject to the same linear dose constraints (e.g., minimum target EUD, maximum critical structure dose) were compared: the original linear model, which use pre-treatment perfusion-weighted mean liver dose only, and a new model designed to directly optimize the predicted post-treatment global liver function. The latter, a highly non-linear non-convex problem, was approximately solved by optimizing a piece-wise linear approximation of the objective function with a customized mixed-integer linear programming (MILP) based algorithm. 2D synthetic and 3D clinical cases were studied to assess the potential benefit of the new model.
Results: Compared to those of the original model, dose distributions for the functional dose response model take advantage of voxels with saturated damage to deliver the required dose to the target instead of inflicting additional damage on other well-functioning voxels. This leads to an improvement in predicted post-treatment global liver function. The customized solution method also improves optimization efficiency compared to solving the MILP conventionally.
Conclusion: Functional imaging such as DCE-MRI can be used during treatment planning to maximize potential post-treatment function. However, simple approximations such as minimization of perfusion-weighted mean dose may not be sufficient. Alternatively, a dose-response model that reflects response threshold and saturation effects was shown to improve the prediction of post-treatment function in liver SBRT.
Funding Support, Disclosures, and Conflict of Interest: Supported by P01 CA 059827
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