Using a knowledge-based planning solution to select patients for proton therapy

Background and purpose Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. Material and methods Model_PROT and Model_PHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted Model_PHOT mean dose minus predicted Model_PROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted Model_PROT/Model_PHOT mean dose R² was 0.95/0.98. Generally, achieved mean dose for Model_PHOT/Model_PROT KBPs was respectively lower/higher than predicted. Comparing Model_PROT/Model_PHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2 Gy, on average. ΔPrediction ≥ 6 Gy correctly selected 4 of 5 patients for protons. Conclusions Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.