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A Robust Approach to Dose Mimicking

A. Babier*, M. McGrail , A. L. McNiven , T. C. Y. Chan , University of Toronto, Toronto, ON


(Sunday, 7/29/2018) 5:05 PM - 6:00 PM

Room: Room 209

Purpose: To develop a clinically implementable workflow for our robust dose mimicking pipeline that automatically generates treatment plans using knowledge-based planning (KBP).

Methods: First, we used a random forest KBP model to predict 3D dose distributions for each patient in a large dataset of 217 oropharynx patients using leave-one-out cross-validation. Second, the predicted dose distributions were input into a novel robust dose mimicking (RDM) method to generate plans (RDM-P plans). To facilitate a baseline comparison we also generate “RDM-C� plans using clinical dose distributions as a surrogate for perfect KBP predictions. The RDM-P plans were constrained to the same fluence heterogeneity as RDM-C plans. We compared the predictions and RDM-P plans against the RDM-C plans using the gamma metric (3%/3mm) and clinical planning criteria.

Results: RDM-P plans were generally non-inferior to RDM-C plans, and between the plans 86% of voxels passed the gamma criteria. On average, the RDM-P plans improved the clinical criteria by 0.8Gy over the RDM-C plans; average dose to the OARs was also reduced by 1.2Gy in the RDM-P plans. Overall clinical criteria satisfaction was comparable between RDM-P and RDM-C plans (77% vs. 79%). Additionally, RDM-P plans were somewhat desensitized to prediction errors from the KBP model. For example, the 62% of voxels in the predicted dose distributions passed the gamma criteria for the RDM-C plans, but once those predictions were input to the RDM model they generated plans where 86% of the voxels passed the criteria.

Conclusion: Clinical plans represent the gold-standard for input to robust dose mimicking, but we show that KBP predictions are a viable (and clinically more reasonable) alternative. We illustrate that using either KBP predictions or clinical plans as input to RDM can generate similar high quality plans.


Inverse Planning, Optimization, Treatment Planning


TH- External beam- photons: treatment planning/virtual clinical studies

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