Cancers, Vol. 10, Pages 420: Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Cancers, Vol. 10, Pages 420: Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Cancers doi: 10.3390/cancers10110420

Authors: Alexander R. Delaney Lei Dong Anthony Mascia Wei Zou Yongbin Zhang Lingshu Yin Sara Rosas Jan Hrbacek Antony J. Lomax Ben J. Slotman Max Dahele Wilko F. A. R. Verbakel

Background: Radiotherapy treatment planning as an 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 need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

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