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Automatic replanning of VMAT plans for different treatment machines: A template-based approach using constrained optimization

Automatische Umplanung von VMAT-Plänen für verschiedene Bestrahlungsgeräte: Ein Template-basierter Ansatz mit beschränkter Optimierung

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Abstract

Purpose

To investigate a new automatic template-based replanning approach combined with constrained optimization, which may be highly useful for a rapid plan transfer for planned or unplanned machine breakdowns. This approach was tested for prostate cancer (PC) and head-and-neck cancer (HNC) cases.

Methods

The constraints of a previously optimized volumetric modulated arc therapy (VMAT) plan were used as a template for automatic plan reoptimization for different accelerator head models. All plans were generated using the treatment planning system (TPS) Hyperion. Automatic replanning was performed for 16 PC cases, initially planned for MLC1 (4 mm MLC) and reoptimized for MLC2 (5 mm) and MLC3 (10 mm) and for 19 HNC cases, replanned from MLC2 to MLC3. EUD, Dmean, D2%, and D98% were evaluated for targets; for OARs EUD and D2% were analyzed. Replanning was considered successful if both plans fulfilled equal constraints.

Results

All prostate cases were successfully replanned. The mean relative target EUD deviation was −0.15% and −0.57% for replanning to MLC2 and MLC3, respectively. OAR sparing was successful in all cases. Replanning of HNC cases from MLC2 to MLC3 was successful in 16/19 patients with a mean decrease of −0.64% in PTV60 EUD. In three cases target doses were substantially decreased by up to −2.58% (PTV60) and −3.44% (PTV54), respectively. Nevertheless, OAR sparing was always achieved as planned.

Conclusions

Automatic replanning of VMAT plans for a different treatment machine by using pre-existing constraints as a template for a reoptimization is feasible and successful in terms of equal constraints.

Zusammenfassung

Ziele

In dieser Studie wurde ein neuer Template-basierter Ansatz zur automatischen Umplanung von Bestrahlungsplänen mit beschränkter Optimierung untersucht, der für die schnelle Planübertragung im Fall von planmäßigen und außerplanmäßigen Maschinenausfällen von großem Nutzen sein könnte. Der Ansatz wurde für Prostatakarzinom (PK) und Kopf-Hals-Tumor (HNO) Fälle getestet.

Methoden

Die Beschränkungen eines vorher optimierten Volumetric-modulated-arc-therapy(VMAT)-Plans wurden als Template für die automatische Reoptimierung mit einem anderen Strahlerkopfmodell genutzt. Alle Pläne wurden im Bestrahlungsplanungsprogramm Hyperion erstellt. 16 PK-Fälle, die ursprünglich für den Multi-Leaf-Kollimator MLC1 (4 mm MLC) geplant waren, wurden automatisch auf MLC2 (5 mm) und MLC3 (10 mm) umgeplant. Für 19 HNO-Fälle erfolgte die Umplanung von MLC2 auf MLC3. Für Zielvolumen (PTV) wurden die „equivalent uniform dose“ (EUD), DMean, D2 % und D98 % ausgewertet, für Risikoorgane EUD und D2 %. Eine Umplanung galt als erfolgreich, wenn beide Pläne gleiche Beschränkungen erfüllten.

Ergebnisse

Alle PK-Fälle konnten erfolgreich automatisch umgeplant werden. Die mittlere relative Abweichung der PTV EUD betrug −0,15  % (MLC2) und −0,57 % (MLC3). Die Umplanung von MLC2 auf MLC3 war in 16 von 19 HNO-Fällen erfolgreich. Die EUD im PTV60 nahm dabei durchschnittlich um −0,64 % ab. In 3 Fällen wurden erhebliche Dosiseinbußen von bis zu −2,58 % (PTV60) bzw. −3,44 % (PTV54) beobachtet. Die Risikoorganschonung konnte jedoch immer wie geplant eingehalten werden.

Schlussfolgerung

Die automatische Umplanung von VMAT-Plänen für ein anderes Bestrahlungsgerät unter Nutzung eines Templates, automatisch generiert aus den Beschränkungen eines bereits existierenden Plans, ist möglich und erfolgreich im Hinblick auf gleichermaßen erfüllte Beschränkungen.

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References

  1. Voet PWJ, Dirkx MLP, Breedveld S et al (2014) Fully automated volumetric modulated arc therapy plan generation for prostate cancer patients. Int J Radiat Oncol Biol Phys 88:1175–1179. https://doi.org/10.1016/j.ijrobp.2013.12.046

    Article  Google Scholar 

  2. Sharfo AWM, Voet PWJ, Breedveld S et al (2015) Comparison of VMAT and IMRT strategies for cervical cancer patients using automated planning. Radiother Oncol 114:395–401. https://doi.org/10.1016/j.radonc.2015.02.006

    Article  Google Scholar 

  3. Della Gala G, Dirkx MLP, Hoekstra N et al (2017) Fully automated VMAT treatment planning for advanced-stage NSCLC patients. Strahlenther Onkol 193:402–409. https://doi.org/10.1007/s00066-017-1121-1

    Article  PubMed  PubMed Central  Google Scholar 

  4. Buschmann M, Sharfo AWM, Penninkhof J et al (2017) Automated volumetric modulated arc therapy planning for whole pelvic prostate radiotherapy. Strahlenther Onkol. https://doi.org/10.1007/s00066-017-1246-2

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gintz D, Latifi K, Caudell J et al (2016) Initial evaluation of automated treatment planning software. J Appl Clin Med Phys 17:331–346. https://doi.org/10.1120/jacmp.v17i3.6167

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hansen CR, Bertelsen A, Hazell I et al (2016) Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans. Clin Transl Radiat Oncol 1:2–8. https://doi.org/10.1016/j.ctro.2016.08.001

    Article  PubMed  PubMed Central  Google Scholar 

  7. Speer S, Klein A, Kober L et al (2017) Automation of radiation treatment planning. Strahlenther Onkol. https://doi.org/10.1007/s00066-017-1150-9

    Article  PubMed  Google Scholar 

  8. Kusters JMAM, Bzdusek K, Kumar P et al (2017) Automated IMRT planning in pinnacle. Strahlenther Onkol 193:1031–1038. https://doi.org/10.1007/s00066-017-1187-9

    Article  CAS  PubMed  Google Scholar 

  9. Tol JP, Delaney AR, Dahele M et al (2015) Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys 91:612–620. https://doi.org/10.1016/j.ijrobp.2014.11.014

    Article  PubMed  Google Scholar 

  10. Schubert C, Waletzko O, Weiss C et al (2017) Intercenter validation of a knowledge based model for automated planning of volumetric modulated arc therapy for prostate cancer. The experience of the German RapidPlan Consortium. PLoS ONE 12:1–13. https://doi.org/10.1371/journal.pone.0178034

    Article  CAS  Google Scholar 

  11. Li N, Zarepisheh M, Uribe-Sanchez A et al (2013) Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs. Phys Med Biol 58:8725–8738. https://doi.org/10.1088/0031-9155/58/24/8725

    Article  PubMed  Google Scholar 

  12. Zarepisheh M, Long T, Li N et al (2014) A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. Med Phys. https://doi.org/10.1118/1.4875700

    Article  PubMed  Google Scholar 

  13. Song T, Li N, Zarepisheh M et al (2016) An automated treatment plan quality control tool for intensity-modulated radiation therapy using a voxel-weighting factor-based re-optimization algorithm. PLoS ONE 11:e149273. https://doi.org/10.1371/journal.pone.0149273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. McIntosh C, Welch M, McNiven A et al (2017) Fully automated treatment planning for head and neck radiotherapy using voxel-based dose prediction and dose mimicking method. Phys Med Biol 62:5926. https://doi.org/10.1088/1361-6560/aa71f8

    Article  PubMed  Google Scholar 

  15. McIntosh C, Purdie TG (2017) Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning. Phys Med Biol 62:415. https://doi.org/10.1088/1361-6560/62/2/415

    Article  PubMed  Google Scholar 

  16. Breedveld S, Storchi PRM, Keijzer M et al (2007) A novel approach to multi-criteria inverse planning for IMRT. Phys Med Biol 52:6339–6353. https://doi.org/10.1088/0031-9155/52/20/016

    Article  PubMed  Google Scholar 

  17. Breedveld S, Storchi PRM, Voet PWJ, Heijmen BJM (2012) iCycle: integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Med Phys 39:951–963. https://doi.org/10.1118/1.3676689

    Article  Google Scholar 

  18. Winkel D, Bol GH, van Asselen B et al (2016) Development and clinical introduction of automated radiotherapy treatment planning for prostate cancer. Phys Med Biol 61:8587–8595. https://doi.org/10.1088/1361-6560/61/24/8587

    Article  CAS  PubMed  Google Scholar 

  19. Xhaferllari I, Wong E, Bzdusek K et al (2013) Automated IMRT planning with regional optimization using planning scripts. J Appl Clin Med Phys 14:4052. https://doi.org/10.1120/jacmp.v14i1.4052

    Article  PubMed  Google Scholar 

  20. Alber M (2008) Normal tissue dose-effect models in biological dose optimisation. Z Med Phys 18:102–110. https://doi.org/10.1016/j.zemedi.2007.08.002

    Article  PubMed  Google Scholar 

  21. Alber M, Birkner M, Nüsslin F (2002) Tools for the analysis of dose optimization: II. Sensitivity analysis. Phys Med Biol 47:N265

    Article  CAS  Google Scholar 

  22. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (https://www.R-project.org/)

    Google Scholar 

  23. Kerkhof EM, Raaymakers BW, van der Heide UA et al (2008) Online MRI guidance for healthy tissue sparing in patients with cervical cancer: an IMRT planning study. Radiother Oncol 88:241–249. https://doi.org/10.1016/j.radonc.2008.04.009

    Article  PubMed  Google Scholar 

  24. Bohoudi O, Bruynzeel AME, Senan S et al (2017) Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer. Radiother Oncol. https://doi.org/10.1016/j.radonc.2017.07.028

    Article  PubMed  Google Scholar 

  25. Ottosson RO, Engström PE, Sjöström D et al (2009) The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques. Acta Oncol (Madr) 48:233–237. https://doi.org/10.1080/02841860802251559

    Article  CAS  Google Scholar 

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Funding

This study was funded by the Medical Faculty of the University of Tübingen Fortüne Grant Nr. 2414-0-0.

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Authors and Affiliations

  1. Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany

    Luise A. Künzel & Daniela Thorwarth

  2. Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany

    Oliver S. Dohm

  3. Radiation Oncology, University Hospital Heidelberg, 69120, Heidelberg, Germany

    Markus Alber

  4. Department of Radiation Oncology, University Hospital Tübingen, 72076, Tübingen, Germany

    Daniel Zips

  5. German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Daniel Zips & Daniela Thorwarth

Authors
  1. Luise A. Künzel
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  2. Oliver S. Dohm
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  3. Markus Alber
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  4. Daniel Zips
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Corresponding author

Correspondence to Daniela Thorwarth.

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Conflict of interest

M. Alber has worked as a consultant for Elekta, AB (Sweden) for the development of the Monaco TPS. D. Zips and D. Thorwarth have research collaborations with Elekta, AB (Sweden) and Siemens Healthineers (Germany). L.A. Künzel and O.S. Dohm declare that they have no competing interests.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors. Consent was obtained from all patients identifiable from images or other information within the manuscript. In the case of underage patients, consent was obtained from a parent or legal guardian.

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Künzel, L.A., Dohm, O.S., Alber, M. et al. Automatic replanning of VMAT plans for different treatment machines: A template-based approach using constrained optimization. Strahlenther Onkol 194, 921–928 (2018). https://doi.org/10.1007/s00066-018-1319-x

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  • DOI: https://doi.org/10.1007/s00066-018-1319-x

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