Program Information
Dose Interval Volume Constraint Based Robust Optimization of Intensity-Modulated Proton Therapy
J Shan1*, W Liu2 , Y An3 , M Schild4 , M Bues5 , (1) Arizona State University, Phoenix, AZ, (2) Mayo Clinic Arizona, Phoenix, AZ, (3) Mayo Clinic, Phoenix, AZ, (4) Mayo Clinic Arizona, Phoenix, Arizona, (5) Mayo Clinic Arizona, Phoenix, AZ
Presentations
SU-K-108-16 (Sunday, July 30, 2017) 4:00 PM - 6:00 PM Room: 108
Purpose: Intensity-Modulated Proton Therapy (IMPT) is sensitive to patient setup and proton beam range uncertainties. Previous robust optimization methods like voxel-wise worst-case robust optimization can produce plans resilient to those uncertainties. However, they lack means of controlling the balance between nominal plan quality and plan robustness depending on varying patient-specific clinical priority, which physicians may desire to possess. We propose a new robust optimization method, which is based on dose-interval (DI) volume constraints to explicitly control the balance.
Methods: Dose distributions corresponding to nominal and multiple extreme scenarios associated with patient setup and proton beam range uncertainties were recalculated accordingly. For each voxel, the DI was calculated by the full variation of the doses for each voxel (DI = maximum dose – minimum dose). Then we calculated DI Volume Histogram (DIVH) curves, under which the areas were calculated to quantify plan robustness. DI Volume Constraints (DIVCs) on the target could be further incorporated into the objective function to specify the desired plan robustness. Users could explicitly control the tradeoff between plan robustness and nominal plan quality by modifying parameters of DIVC. We benchmarked our method on one lung case and one H&N case by comparing results to the conventional voxel-wise worst-case robust optimization.
Results: Our new method achieved comparable plan robustness and comparable nominal plan quality with the conventional voxel-wise worst-case robust optimization. Modifying the DIVC controlled the balance between plan robustness and nominal plan quality.
Conclusion: The DIVC-based robust optimization can generate IMPT plans comparable to ones generated by the conventional voxel-wise worst-case robust optimization. And it is an effective method, which enables physicians to control the balance between nominal plan quality and plan robustness.
Funding Support, Disclosures, and Conflict of Interest: Supported by the National Cancer Institute (NCI) Career Developmental Award K25-CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation.
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