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IMRT Planning Quality Control - Automatically Improving Clinical Plans

T Long

T Long1*, M Folkerts2 , S Jiang1 , W Lu1 , (1) UT Southwestern Medical Center, Dallas, TX, (2) Varian Medical Systems, Palo Alto, CA


MO-L-GePD-T-6 (Monday, July 31, 2017) 1:15 PM - 1:45 PM Room: Therapy ePoster Lounge

Purpose: The goal of treatment plan optimization is to generate a high-quality, deliverable treatment plan. However, conventional TPS optimizers often only consider achieving specific dosimetric goals rather than pushing all dose towards ideal values during and after reaching those metrics. We study the use of threshold-driven optimization for reference-based auto-planning (TORA), a DVH-driven plan optimization technique, as a means of automatic quality control for clinical IMRT treatment plans.

Methods: The TORA methodology spatially assigns reference DVH values to specific voxels, then pushes dose toward ideal values (0 for OARs, Prescription for PTVs) while maintaining the input reference DVH. Starting with an existing plan means this initial spatial assignment of the original DVH is feasible and achievable. TORA allows for the clinical DVH to be improved, if possible, resulting in a plan much closer to the Pareto surface. We can measure the improvement gained in applying TORA to existing plans, then reassess plans that significantly change in summary dose (DVH) or spatial dose under this framework.

Results: Quality control TORA was applied to 65 historical prostate patients. While the majority of plans did not significantly improve, there were multiple instances of mild improvement and one case of major improvement that would have led to re-planning.

Conclusion: Most treatment planning optimization techniques operate using non-convex objectives and attempt to achieve target, but not necessarily ideal, values. This can lead to plans generated to “meet criteria,” rather than “meet criteria and do better if possible.” This type of technique can be viewed as a post-processing improvement of existing plans. TORA used for quality control helps identify and improve existing plans.

Funding Support, Disclosures, and Conflict of Interest: Work partially funded by CPRIT RP150485

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