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

Volumn 12, Issue 5, 2017, Pages

  Schubert, Carolin ^a   Waletzko, Oliver ^b   Weiss, Christian ^c   Voelzke, Dirk ^d   Toperim, Sevda ^a   Roeser, Arnd ^e   Puccini, Silvia ^d   Piroth, Marc ^e   Mehrens, Christian ^b   Kueter, Jan Dirk ^f   Hierholz, Kirsten ^c   Gerull, Karsten ^f   Fogliata, Antonella ^g   Block, Andreas ^b   Cozzi, Luca ^g  

^a UNIVERSITY MEDICAL CENTER HAMBURG EPPENDORF   (Germany)

^b KLINIKUM DORTMUND   (Germany)

^c KLINIKUM DARMSTADT   (Germany)

^d Oncology Practice   (Germany)

^e UNIVERSITY OF WITTEN HERDECKE   (Germany)

^f UNIVERSITY HOSPITAL SCHLESWIG HOLSTEIN   (Germany)

^g HUMANITAS CLINICAL AND RESEARCH CENTER   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUTOMATION; COMPUTER MODEL; GERMAN (CITIZEN); HUMAN; MAJOR CLINICAL STUDY; MALE; PROCESS OPTIMIZATION; PROGRAM ACCEPTABILITY; PROSTATE CANCER; RADIATION DOSE; RADIOTHERAPY PLANNING SYSTEM; SAMPLING; STRATIFIED SAMPLE; VALIDATION STUDY; VOLUMETRIC MODULATED ARC THERAPY; CLINICAL TRIAL; INTENSITY MODULATED RADIATION THERAPY; MULTICENTER STUDY; PROCEDURES; PROSTATIC NEOPLASMS; RADIOTHERAPY DOSAGE; THEORETICAL MODEL;

HUMANS; MALE; MODELS, THEORETICAL; PROSTATIC NEOPLASMS; RADIOTHERAPY DOSAGE; RADIOTHERAPY PLANNING, COMPUTER-ASSISTED; RADIOTHERAPY, INTENSITY-MODULATED;

EID: 85019868236     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0178034     Document Type: Article

References (24)

1. Chanyavanich V, Das S, Lee W, Lo W. Knowledge based IMRT treatment planning for prostate cancer. Med. Phys. 2011; 38:2515-2522 https://doi.org/10.1118/1.3574874 PMID: 21776786.
2. Zhu X, Ge Y, Li T, Thongphiew D, Yin FF, Wu J. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys 2011; 38:719-726. https://doi.org/10.1118/1.3539749 PMID: 21452709.
3. Yuan L, Ge Y, Lee W, Yin FF, Kirkpatrick J, Wu J. Quantitative analysis of the factors which affect the inter-patient organ-At risk dose sparing variation in IMRT plans. Med Phys 2012; 39:6868-6878 https://doi.org/10.1118/1.4757927 PMID: 23127079.
4. Lian J, Yuan L, Ge Y, Chera B, Yoo D, Chang S et al. Modeling the dosimetry of organ-At-risk in head and neck IMRT planning: An inter-Technique and inter-institutional study. Med Phys 2013; 40:121704. https://doi.org/10.1118/1.4828788 PMID: 24320490.
5. Good D, Lo J, Lee R, Wu J, Yin FF, Das S. A knowledge based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: An example application to prostate cancer planning. Int. J. Radiat. Oncol. Biol. Phys. 2013; 87:176-181 https://doi.org/10.1016/j.ijrobp.2013.03.015 PMID: 23623460.
6. Moore K, Scott Brame R, Low D, Mutic S. Experience based quality control of clinical intensity modulated radiotherapy planning. Int. J. Radiat. Oncol. Biol. Phys. 2011; 81:545-551 https://doi.org/10.1016/j.ijrobp.2010.11.030 PMID: 21277097.
7. Appenzoller L, Michalski J, Thorstad W, Mutic S, Moore K. Predicting dose-volume histograms for organs-At-risk in IMRT planning. Med Phys 2012; 39:7446-7461. https://doi.org/10.1118/1.4761864 PMID: 23231294.
8. Fogliata A., Wang P, Belosi F, Clivio A, Nicolini G, Vanetti E et al. Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer. Radiat. Oncol. 2014; 9:236 https://doi.org/10.1186/s13014-014-0236-0 PMID: 25348465.
9. Fogliata A., Belosi F, Clivio A, Navarria P, Nicolini G, Scorsetti M et al On the pre-clinical validation of a commercial model-based optimization engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiother. Oncol. 2014; 113:385-91 https://doi.org/10.1016/j.radonc.2014.11.009 PMID: 25465726.
10. Fogliata A, Nicolini G, Clivio A, Vanetti A, Laksar S, Tozzi A et al. A broad scope knowledge based model for optimization of VMAT in esophageal cancer: validation and assessment of plan quality among different treatment centers. Radiat. Oncol. 2015; 10:220 https://doi.org/10.1186/s13014-015-0530-5 PMID: 26521015.
11. Fogliata A, Nicolini G, Bourgier C, Clivio A, De Rose, F, Fenoglietto P et al. Performance of a KB model for optimization of VMAT plans for single and bilateral breast irradiation. PlosOne. 2015; 10:e0145137.
12. Delaney A, Tol J, Dahele M, Cuijpers J, Slotman B, Verbakel W. Effect of dosimetric outliers on the performance of a commercial knowledge-based planning solution. Int. J. Radiat. Oncol. Biol. Phys. 2016; 94:469-77 https://doi.org/10.1016/j.ijrobp.2015.11.011 PMID: 26867876.
13. Tol JP, Dahele M, Delaney A, Slotman B, Verbakel W. Can knowledge based DVH prediction be used for automated, individualized quality assurance of radiotherapy treatments. Radiat. Oncol. 2015; 10:234 https://doi.org/10.1186/s13014-015-0542-1 PMID: 26584574.
14. Delaney A, Dahele M, tol J, Slotman B, Verbakel W. Knowledge based planning for stereotactic radiotherapy of peripheral early stage lung cancer. Acta Oncol. 2017.
15. Chang A, Hung A, Cheung F, Lee M, Chan O, Philips H et al. Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in NPC patients using IMRT. Int. J. Radiat. Oncol. Biol. Phys. 2016; 95:981-90 https://doi.org/10.1016/j.ijrobp.2016.02.017 PMID: 27302513.
16. Wu H, Jiang f, Yue H, Li S, Zhang Y. A dosimetric evaluation of knowledge based VMAT planning with simultaneous integrated boosting for rectal cancer patients J. Appl. Clin. Med. Phys. 2016; 17:6410.
17. Wu H, Jiang F, Yue H, Zhng H, Wang K, Zhang Y. Applying a RapidPlan model trained on a technique and orientation to another: A feasibility and dosimetric evaluation Radiat Oncol 2016; 11:108 https://doi.org/10.1186/s13014-016-0684-9 PMID: 27538431.
18. Hussein M, South C, Barry M, Adams E, Jordan T, Stewart A et al. Clinical validation and benchmarking of knowledge based IMRT and VMAT treatment planning in pelvic anatomy. Radiother. Oncol. 2016; 120:473-479 https://doi.org/10.1016/j.radonc.2016.06.022 PMID: 27427380.
19. Snyder K, Kim J, Reding A, Fraser C, Gordon J, Ailouni M et al. Development and evaluation of a clinical model for lung cancer patients using stereotactic body radiotherapy (SBRT) within a knowledge based algorithm for treatment planning. J. Appl. Clin. Med. Phys 2016; 17:6429 PMID: 27929499.
20. Berry S., Ma R, Boczkowski A, Jackson A, Zhang P, Hunt M. Evaluating inter campus plan consistency using a knowledge based planning model Radiother. Oncol. 2016; 120:349-55.
21. Li N, Carmona R, Sirak I, Kasaova L, Followill D, Michalski J et al. Highly efficient training, refinement and validation of a knowledge based planning quality control system for radiation therapy clinical trials. Int. J. Radiat. Oncol. Biol. Phys. 2017; 97:164-172 https://doi.org/10.1016/j.ijrobp.2016.10.005 PMID: 27979445.
22. Boero I, Paravati A, Xu B, Cohen E, Mell L, Le Q et al. Importance of radiation oncologist experience among patients with head and neck cancer treated with intensity modulated radiation therapy. J. Clin. Oncol. 2016; 34:684-90 https://doi.org/10.1200/JCO.2015.63.9898 PMID: 26729432.
23. Pesce G, Clivio A, Cozzi L, Nicolini G, Richetti A, Salati E et al. Early clinical experience o radiotherapy of prostate cancer with volumetric modulated arc therapy. Radiat. Oncol. 2010; 5:54 https://doi.org/10.1186/1748-717X-5-54 PMID: 20550722.
24. Aznar M, Petersen P, Logadottir A, Lindberg H, Korreman S, Kjaer-Kristoffersen F et al. Rotational radiotherapy for prostate cancer in clinical practice. Radiother. Oncol. 2010; 97:480-4. https://doi.org/10.1016/j.radonc.2010.09.014 PMID: 20970215.