Evaluation of an Automated Proton Planning Solution

Abstract

PurposeIntensity-modulated proton therapy (IMPT) treatments are increasing, however, treatment planning remains complex and prone to variability. RapidPlanTMPT (Varian Medical Systems, Palo Alto, California, USA) is a pre-clinical, proton-specific, automated knowledge-based planning solution which could reduce variability and increase efficiency. It uses a library of previous IMPT treatment plans to generate a model which can predict organ-at-risk (OAR) dose for new patients, and guide IMPT optimization. This study details and evaluates RapidPlanTMPT.MethodsIMPT treatment plans for 50 head-and-neck cancer patients populated the model-library. The model was then used to create knowledge-based plans (KBPs) for 10 evaluation-patients. Model quality and accuracy were evaluated using model-provided OAR regression plots and examining the difference between predicted and achieved KBP mean dose. KBP quality was assessed through comparison with respective manual IMPT plans on the basis of boost/elective planning target volume (PTVB/PTVE) homogeneity and OAR sparing. The time to create KBPs was recorded.ResultsModel quality was good, with an average R2 of 0.85 between dosimetric and geometric features. The model showed high predictive accuracy with differences of <3 Gy between predicted and achieved OAR mean doses for 88/109 OARs. On average, KBPs were comparable to manual IMPT plans with differences of <0.6% in homogeneity. Only 2 of 109 OARs in KBPs had a mean dose >3 Gy more than the manual plan. On average, dose-volume histogram (DVH) predictions required 0.7 minutes while KBP optimization and dose calculation required 4.1 minutes (a ‘continue optimization’ phase, if required, took an additional 2.8 minutes, on average).ConclusionsRapidPlanTMPT demonstrated efficiency and consistency and IMPT KBPs were comparable to manual plans. Because worse OAR sparing in a KBP was not always associated with geometric-outlier warnings, manual plan checks remain important. Such an automated planning solution could also assist in clinical trial quality assurance and overcome the learning curve associated with IMPT.

Model based patient pre-selection for intensity-modulated proton therapy (IMPT) using automated treatment planning and machine learning

“…An 80% prediction accuracy (32/40) was achieved. Delaney et al used knowledge-based planning and mean doses in various organs for IMPT [14][15][16]. While no translation to NTCP was made, an overall difference between manual and predicted mean doses of 0.8 ± 4.5 Gy was reported.…”

“…Several authors have investigated knowledge-based automated planning for IMPT [14][15][16]. Although implemented in a commercial treatment planning system (TPS), to our knowledge this has not been clinically implemented as of yet.…”

Volumetric modulated arc therapy versus intensity-modulated proton therapy in neoadjuvant irradiation of locally advanced oesophageal cancer

“…We aimed to minimise this risk paying meticulous attention to the IMPT plans. In future, the availability of RapidPlan also for protons [50] might further reduce this potential bias, and we aim to investigate this aspect as a future step in the research program.…”

Ultra-High Dose Rate Transmission Beam Proton Therapy for Conventionally Fractionated Head and Neck Cancer: Treatment Planning and Dose Rate Distributions

“…Two-arc photon VMAT-plans were made using RapidPlan™ (Varian Medical Systems) [ 30 ]. The IMPT-plans used a clinically acceptable three-beam arrangement (gantry angles 45°, 180° and 315°), previously associated with satisfactory plan quality [ 31 , 32 ]. Since TB-plans deliver a dose along the spot track that varies relatively little with depth and do not benefit from the Bragg-peak, a larger number of beams will be needed to achieve sufficient OAR sparing.…”