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Generate Clinical Acceptable Trade-Off Options in Brain IMRT Planning by Local Multi-Criteria Optimization (MCO) Method

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L Yuan

L Yuan1*, Y Ge2 , Y Sheng3 , Q. Jackie Wu4 , (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC, (3) Duke University, Durham, NC, (4) Duke University Medical Center, Durham, NC


SU-F-T-341 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose:we present a method to generate a set of treatment plans with clinical manageable dosimetric tradeoff options in brain IMRT planning guided by knowledge model prediction.

Methods: Multi-criteria optimization (MCO) methods have been developed in RT planning to help physicians make the complex clinical tradeoff decision among different organ sparing goals. In local MCO method, a clinical acceptable tradeoff range is first predicted based on past planning experiences and the specific patient’s anatomy. The anchor points of the local Pareto surface (PS) are obtained from the model prediction. The Pareto front is further refined by varying the objective within the acceptable trade-off range in plan optimization to generate additional point between the anchor points. The quality and efficiency of this method are validated by brain IMRT planning in this study. The knowledge model was first trained by 15 clinical brain IMRT plans. Then it is utilized to guide the Pareto front search for another set of 5 patient cases. We studied the number of plan optimizations needed in order to generate the Pareto surface with sufficient precision which is indicated by the mean minimum distance between the local PS and the global PS.

Results:Local PS can be generated efficiently within the clinical tradeoff range. Using only two anchor plans, the mean minimum distance between the local and global PS in the PTV homogeneity-brainstem median dose (D50) objective space for the 5 validation cases are: ~0.6% to 2% and 0.3% to 1% of prescription dose in terms of PTV dose homogeneity and brainstem D50, respectively. The tradeoff ranges covered by the local PS vary with patient anatomy, which are 10-20% and 40-80% on average for the two dosimetric parameters.

Conclusion:By combining knowledge model and MCO, the local MCO method can generate clinical optimal tradeoff options efficiently.

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