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

Alexander Delaney,Anthony Mascia,Yongbin Zhang,Wei Zou,Jan Hrbacek,Sara Rosas,Lei Dong,Wilko Verbakel,Ben Slotman,Max Dahele,Lingshu Yin,Antony Lomax

doi:10.3390/cancers10110420

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.

Highlights

The number of proton centers is increasing [1]
Target coverage and homogeneity were similar between knowledge-based plan (KBP) and benchmark plans
This study, aimed at providing proof-of-principle, demonstrated that a knowledge-based planning solution, using a model library comprising plans from a single center with a standard planning technique, could create good quality head and neck cancer (HNC) intensity-modulated proton therapy (IMPT) plans when benchmarked against plans from three experienced proton centers

Summary

Introduction

The number of proton centers is increasing [1]. treatment planning studies for established photon modalities such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) indicate substantial intra- and inter-institutionalCancers 2018, 10, 420; doi:10.3390/cancers10110420 www.mdpi.com/journal/cancersCancers 2018, 10, 420 variation [2]. A desire to reduce variation, and produce consistently “good” plans, has largely spurred the development of automated treatment planning for photon therapy [7,8,9,10]. One such approach is knowledge-based planning, an example of which is RapidPlanTM (Varian Medical Systems, Palo Alto, CA). We benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. 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. 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

Methods

Results

Conclusion