Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Abstract

Background: Radiotherapy treatment planning is increasingly automated and knowledge-based planning has been shown to match and sometimes improve upon manual clinical plans, with increased consistency and efficiency. In this study, we benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. Methods: A model library was constructed, comprising 50 head and neck cancer (HNC) manual IMPT plans from a single center. Three external-centers each provided seven manual benchmark IMPT plans. A knowledge-based plan (KBP) using a standard beam arrangement for each patient was compared with the benchmark plan on the basis of planning target volume (PTV) coverage and homogeneity and mean organ-at-risk (OAR) dose. Results: PTV coverage and homogeneity of KBPs and benchmark plans were comparable. KBP mean OAR dose was lower in 32/54, 45/48 and 38/53 OARs from center-A, -B and -C, with 23/32, 38/45 and 23/38 being >2 Gy improvements, respectively. In isolated cases the standard beam arrangement or an OAR not being included in the model or being contoured differently, led to higher individual KBP OAR doses. Generating a KBP typically required <10 min. Conclusions: A knowledge-based IMPT planning solution using a single-center model could efficiently generate plans of comparable quality to manual HNC IMPT plans from centers with differing planning aims. Occasional higher KBP OAR doses highlight the need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

Evaluation of an Automated Proton Planning Solution

“…Additionally, all IMPT plans in this study were non-robustly optimized. While preliminary results on the use of RapidPlan TM PT in creating robustly optimized IMPT plans for external proton centers are promising [19], a dedicated investigation should be considered in future to accommodate the increasing interest in robust optimization. Finally, in this investigation, we report on the physical dose.…”

Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy

“…It is important to note that in all patients included in the current study, model-based optimized plans were created for both VMAT and IMPT [16]. As the quality of the VMAT and IMPT plans, optimization strategies, dose scheduling and patient characteristics are expected to differ widely across centres, the models presented in this study may not be valid for use in other centres, as both regression coefficients of the parameters in the models as well as the level of rescaling is expected to differ from center to center [17][18][19][20][21]. Also within institutions, or specific subgroups of patients, inter-patient variance could be larger and the performance and applicability of any model could be reduced.…”

“…In model-based selection of patients for proton therapy, machine learning methods and knowledgebased dose predictions can also be used to predict plan comparison outcome of new patients by learning from previous ones who have already a plan comparison. A number of authors reported on knowledge-based dose predictions and automated planning for both photons and protons with a high accuracy and strong correlations between predicted and manual dose distributions [17,[22][23][24][25][26].…”

“…Although these are fast and highly precise methods, they may accommodate the same drawbacks associated with a small dataset lacking in variation, as some of our methods do. Because they remain largely dependent on the plans based on which the models were created and thus are also prone to be affected by the consistency of the plans which is hard to achieve with inter-center and even inter-planner variability [17,28].…”

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

“…Spot weights for both proton plans were optimized using multi-field optimization (MFO) with objectives for PTVB, PTVE, PTVO, multiple OARs, and several rings around the PTVs to avoid hotspots. For the IMPT-plans line objectives were set in the optimizer using RapidPlanPT™ (Varian Medical Systems) with a 50-patient, 3-field IMPT plan model [ 33 ]. The same RapidPlanPT™ predictions were used for TB-plans, with the exception that mean dose objectives were used for most OARs, since the TB optimizer does not accept line objectives.…”