Vol 26, No 6 (2021)
Research paper
Published online: 2021-08-12

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Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study

Tatsuya Kamima1, Yoshihiro Ueda2, Jun-ichi Fukunaga3, Mikoto Tamura4, Yumiko Shimizu5, Yuta Muraki5, Yasuo Yoshioka1, Nozomi Kitamura1, Yuya Nitta2, Masakazu Otsuka4, Hajime Monzen4
Rep Pract Oncol Radiother 2021;26(6):849-860.

Abstract

Background: The aim of this study was to investigate the performance of the RapidPlan (RP) using models registered pseudo-structures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer.

Materials and methods: Pseudo-structures around the PTV were retrospectively contoured for patients who had completed treatment at five institutions. For 22 common patients, plans were generated with a single optimization for models with two (RP_2), four (RP_4), and five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations.

Results: Most dosimetric parameters showed no major differences between each RP model. In particular, the rectum Dmax, V50Gy, and V40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. The average proportions of plans achieving acceptable criteria for dosimetric parameters were close to 100% for all models. Using RP_2, the average time for the VMAT planning was reduced by 88 minutes compared with the clinical plan.

Conclusion: The RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time.

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References

  1. Yuan Y, You J, Wang W, et al. Long-term follow-up of volumetric modulated arc therapy in definitive radiotherapy for cervical cancer: A single-center retrospective experience. Radiat Med Protect. 2020; 1(2): 81–87.
  2. Lin Y, Ouyang Y, Chen K, et al. Long-term follow-up of volumetric modulated arc therapy in definitive radiotherapy for cervical cancer: A single-center retrospective experience. Front Oncol. 2019; 9: 760.
  3. Tan LT, Tanderup K, Kirisits C, et al. Image-guided Adaptive Radiotherapy in Cervical Cancer. Semin Radiat Oncol. 2019; 29: 284–98.
  4. Heijkoop S.T. Plan-of-the-day Adaptive Radiotherapy for Locally Advanced Cervical Cancer. Erasmus University Rotterdam, 2017. http://hdl.handle.net/1765/102419 (12 July 2020).
  5. Dial C, Weiss E, Siebers JV, et al. Benefits of adaptive radiation therapy in lung cancer as a function of replanning frequency. Med Phys. 2016; 43(4): 1787.
  6. Fusella M, Scaggion A, Pivato N, et al. Efficiently train and validate a RapidPlan model through APQM scoring. Med Phys. 2018; 45(6): 2611–2619.
  7. Tol JP, Delaney AR, Dahele M, et al. Evaluation of a knowledge-based planning solution for head and neck cancer. Int J Radiat Oncol Biol Phys. 2015; 91(3): 612–620.
  8. Fogliata A, Nicolini G, Clivio 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.
  9. Wu H, Jiang F, Yue H, et al. A dosimetric evaluation of knowledge-based VMAT planning with simultaneous integrated boosting for rectal cancer patients. J Appl Clin Med Phys. 2016; 17(6): 78–85.
  10. Kubo K, Monzen H, Ishii K, et al. Dosimetric comparison of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer. Phys Med. 2017; 44: 199–204.
  11. Kubo K, Monzen H, Ishii K, et al. Inter-planner variation in treatment-plan quality of plans created with a knowledge-based treatment planning system. Phys Med. 2019; 67: 132–140.
  12. Ueda Y, Miyazaki M, Sumida I, et al. Knowledge-based planning for oesophageal cancers using a model trained with plans from a different treatment planning system. Acta Oncol. 2020; 59(3): 274–283.
  13. Uehara T, Monzen H, Tamura M, et al. Dose-volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer. J Radiat Res. 2020; 61(3): 499–505.
  14. Inoue E, Doi H, Monzen H, et al. Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation. In Vivo. 2020; 34(3): 1095–1101.
  15. Tamura M, Monzen H, Matsumoto K, et al. Influence of Cleaned-up Commercial Knowledge-Based Treatment Planning on Volumetric-Modulated Arc Therapy of Prostate Cancer. J Med Phys. 2020; 45(2): 71–77.
  16. Monzen H, Tamura M, Ueda Y, et al. Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study. Radiol Phys Technol. 2020; 13(4): 327–335.
  17. Ueda Y, Monzen H, Fukunaga JI, et al. Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions' models for prostate cancer. Rep Pract Oncol Radiother. 2020; 25(6): 1023–1028.
  18. Ito T, Tamura M, Monzen H, et al. [Impact of Aperture Shape Controller on Knowledge-based VMAT Planning of Prostate Cancer]. Nihon Hoshasen Gijutsu Gakkai Zasshi. 2021; 77(1): 23–31.
  19. Wada Y, Monzen H, Tamura M, et al. Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer. J Med Phys. 2021; 46(1): 7–15.
  20. Tamura M, Monzen H, Matsumoto K, et al. Mechanical performance of a commercial knowledge-based VMAT planning for prostate cancer. Radiat Oncol. 2018; 13(1): 163.
  21. Fung NT, Hung WM, Sze CK, et al. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis. Med Dosim. 2020; 45(1): 60–65.
  22. Fogliata A, Wang PM, Belosi F, 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.
  23. Neve WD, Wu Y, Ezzell G. Practical IMRT Planning. Image-guided IMRT. Springer, Berlin 2006: 49–54.
  24. Castriconi R, Fiorino C, Passoni P, et al. Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer. Phys Med. 2020; 70: 58–64.
  25. Ueda Y, Fukunaga JI, Kamima T, et al. Evaluation of multiple institutions' models for knowledge-based planning of volumetric modulated arc therapy (VMAT) for prostate cancer. Radiat Oncol. 2018; 13(1): 46.
  26. Kamima T, Ueda Y, Fukunaga JI, et al. Multi-institutional evaluation of knowledge-based planning performance of volumetric modulated arc therapy (VMAT) for head and neck cancer. Phys Med. 2019; 64: 174–181.
  27. Murakami N, Isohashi F, Hasumi Y, et al. Single-arm confirmatory trial of postoperative concurrent chemoradiotherapy using intensity modulated radiation therapy for patients with high-risk uterine cervical cancer: Japan Clinical Oncology Group study (JCOG1402). Jpn J Clin Oncol. 2019; 49(9): 881–885.
  28. Okamoto H, Murakami N, Isohashi F, et al. Dummy-run for standardizing plan quality of intensity-modulated radiotherapy for postoperative uterine cervical cancer: Japan Clinical Oncology Group study (JCOG1402). Radiat Oncol. 2019; 14(1): 133.
  29. Toita T, Ohno T, Kaneyasu Y, et al. Japan Clinical Oncology Group. A consensus-based guideline defining the clinical target volume for pelvic lymph nodes in external beam radiotherapy for uterine cervical cancer. Jpn J Clin Oncol. 2010; 40(5): 456–463.
  30. Gay HA, Barthold HJ, O'Meara E, et al. Pelvic normal tissue contouring guidelines for radiation therapy: a Radiation Therapy Oncology Group consensus panel atlas. Int J Radiat Oncol Biol Phys. 2012; 83(3): e353–e362.
  31. Chen J, Chen C, Atwood T, et al. Volumetric modulated arc therapy planning method for supine craniospinal irradiation. J Radiat Oncol. 2012; 1(3): 291–297.
  32. Hussein M, South CP, Barry MA, et al. Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy. Radiother Oncol. 2016; 120(3): 473–479.
  33. Delaney AR, Tol JP, Dahele M, et al. Effect of Dosimetric Outliers on the Performance of a Commercial Knowledge-Based Planning Solution. Int J Radiat Oncol Biol Phys. 2016; 94(3): 469–477.
  34. Kataria T, Sharma K, Subramani V, et al. Homogeneity Index: An objective tool for assessment of conformal radiation treatments. J Med Phys. 2012; 37(4): 207–213.
  35. Feuvret L, Noël G, Mazeron JJ, et al. Conformity index: a review. Int J Radiat Oncol Biol Phys. 2006; 64(2): 333–342.
  36. Fogliata A, Cozzi L, Reggiori G, et al. RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies. Radiat Oncol. 2019; 14(1): 187.
  37. Fogliata A, Reggiori G, Stravato A, et al. RapidPlan head and neck model: the objectives and possible clinical benefit. Radiat Oncol. 2017; 12(1): 73.
  38. Cilla S, Ianiro A, Romano C, et al. Template-based automation of treatment planning in advanced radiotherapy: a comprehensive dosimetric and clinical evaluation. Sci Rep. 2020; 10(1): 423.
  39. Li J, Xu Z, Pilar A, et al. Adaptive radiotherapy for nasopharyngeal carcinoma. Annals of Nasopharynx Cancer. 2020; 4: 1–1.
  40. Acharya S, Fischer-Valuck BW, Kashani R, et al. Online Magnetic Resonance Image Guided Adaptive Radiation Therapy: First Clinical Applications. Int J Radiat Oncol Biol Phys. 2016; 94(2): 394–403.
  41. Bhardwaj A, Kehwar TS, Chakarvarti SK, et al. Variations in inter-observer contouring and its impact on dosimetric and radiobiological parameters for intensity-modulated radiotherapy planning in treatment of localised prostate cancer. J Radiother Pract. 2008; 7(2): 77–88.