TY - JOUR
T1 - Using a knowledge-based planning solution to select patients for proton therapy
AU - Delaney, Alexander R.
AU - Dahele, Max
AU - Tol, Jim P.
AU - Kuijper, Ingrid T.
AU - Slotman, Ben J.
AU - Verbakel, Wilko F.A.R.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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 ModelPROT and ModelPHOT 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 ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT 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.
AB - 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 ModelPROT and ModelPHOT 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 ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6 Gy, and benchmarked using achieved KBP doses. Results Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT 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.
KW - Head and neck cancer
KW - Knowledge-based planning
KW - Patient selection
KW - Proton therapy
UR - http://www.scopus.com/inward/record.url?scp=85017359089&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.radonc.2017.03.020
DO - https://doi.org/10.1016/j.radonc.2017.03.020
M3 - Article
SN - 0167-8140
VL - 124
SP - 263
EP - 270
JO - Radiotherapy and oncology
JF - Radiotherapy and oncology
IS - 2
ER -