Quantifying Motion–Setup–Range Interplay for Hypofractionated IMPT Prostate Plan
Abstract
Purpose
To quantify the impact of a prostate motion model on target robustness under setup and range uncertainty, and to evaluate whether anatomical robust optimization improves robustness while maintaining nominal OAR constraint compliance.
Methods
A retrospective low-risk prostate IMPT plan (36.25 Gy(RBE) in 5 fractions; two opposed lateral fields) was analyzed in RayStation. Motion was modeled using six simulated CT image sets representing systematic ±3 mm shifts in R–L, I–S, and P–A directions plus the nominal CT (7 image sets). Robustness parameters were ±5 mm isotropic setup and ±3.5% range uncertainty. Two approaches were compared: Set 1, a plan robustly optimized on the nominal CT and evaluated across all CTs with setup and range uncertainty (84 scenarios = 7 CTs × 6 setup shifts × 2 density points); and Set 2, anatomical robust optimization incorporating all CTs, (147 optimization scenarios), followed by evaluation on the same 84-scenario space as Set 1. Beam geometry and optimization parameters/objectives/weightings remained same across both sets.
Results
Anatomical robust optimization improved worst-case target coverage relative to nominal-only optimization under motion-inclusive evaluation. For CTV D99%, nominal/worst-case was 36.15/29.94 Gy(RBE) in Set 1 and 36.28/31.09 Gy(RBE) in Set 2. For CTV D95%, nominal/worst-case was 36.48/32.85 Gy(RBE) in Set 1 and 36.59/33.66 Gy in Set 2. Nominal OAR endpoints remained within planning constraints for both approaches; representative nominal values were rectum D3cc 23.58 Gy (Set 1) vs 26.96 Gy (Set 2), bladder D0.03cc 36.67 Gy vs 37.07 Gy, and urethra D0.03cc 36.55 Gy vs 36.55 Gy.
Conclusion
For parallel opposed-lateral prostate IMPT, incorporating a 3-mm motion model into robustness evaluation reduced worst-case target coverage. Anatomical robust optimization partially restores worst-case CTV coverage while maintaining OAR compliance in nominal plans. Larger-cohort studies are warranted to generalize these findings and refine robustness tradeoffs.