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

<b>Background:</b> 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 i...

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Main 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
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Cancers
Subjects:
Online Access:https://www.mdpi.com/2072-6694/10/11/420
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spelling doaj-1e07e98a25a3425ba442d4a016efbf402020-11-24T21:41:37ZengMDPI AGCancers2072-66942018-11-01101142010.3390/cancers10110420cancers10110420Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking StudyAlexander R. Delaney0Lei Dong1Anthony Mascia2Wei Zou3Yongbin Zhang4Lingshu Yin5Sara Rosas6Jan Hrbacek7Antony J. Lomax8Ben J. Slotman9Max Dahele10Wilko F. A. R. Verbakel11Cancer Center Amsterdam, Department of Radiation Oncology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The NetherlandsDepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Radiation Oncology, University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH 45219, USADepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Radiation Oncology, University of Cincinnati Medical Center, 234 Goodman Street, Cincinnati, OH 45219, USADepartment of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USAPaul Scherrer Institute, Center for Proton Radiotherapy, 5232 Villigen, SwitzerlandPaul Scherrer Institute, Center for Proton Radiotherapy, 5232 Villigen, SwitzerlandPaul Scherrer Institute, Center for Proton Radiotherapy, 5232 Villigen, SwitzerlandCancer Center Amsterdam, Department of Radiation Oncology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The NetherlandsCancer Center Amsterdam, Department of Radiation Oncology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The NetherlandsCancer Center Amsterdam, Department of Radiation Oncology, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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 &gt;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 &lt;10 min. <b>Conclusions:</b> 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.https://www.mdpi.com/2072-6694/10/11/420proton therapyIMPThead and neck cancerknowledge-based planningmodel-based planningautomated
collection DOAJ
language English
format Article
sources DOAJ
author 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
spellingShingle 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
Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
Cancers
proton therapy
IMPT
head and neck cancer
knowledge-based planning
model-based planning
automated
author_facet 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
author_sort Alexander R. Delaney
title Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
title_short Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
title_full Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
title_fullStr Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
title_full_unstemmed Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study
title_sort automated knowledge-based intensity-modulated proton planning: an international multicenter benchmarking study
publisher MDPI AG
series Cancers
issn 2072-6694
publishDate 2018-11-01
description <b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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 &gt;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 &lt;10 min. <b>Conclusions:</b> 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.
topic proton therapy
IMPT
head and neck cancer
knowledge-based planning
model-based planning
automated
url https://www.mdpi.com/2072-6694/10/11/420
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