Skip to main content
Log in

Clinical implementation of PerFRACTION™ for pre-treatment patient-specific quality assurance

  • Original Paper - Cross-Disciplinary Physics and Related Areas of Science and Technology
  • Published:
Journal of the Korean Physical Society Aims and scope Submit manuscript

Abstract

This study is to assess the clinical use of commercial PerFRACTION™ for patient-specific quality assurance of volumetric-modulated arc therapy. Forty-six pretreatment verification plans for patients treated using a TrueBeam STx linear accelerator for lesions in various treatment sites such as brain, head and neck (H&N), prostate, and lung were included in this study. All pretreatment verification plans were generated using the Eclipse treatment planning system (TPS). Dose distributions obtained from electronic portal imaging device (EPID), ArcCHECK™, and two-dimensional (2D)/three-dimensional (3D) PerFRACTION™ were then compared with the dose distribution calculated from the Eclipse TPS. In addition, the correlation between the plan complexity (the modulation complexity score and the leaf travel modulation complexity score) and the gamma passing rates (GPRs) of each quality assurance (QA) system was evaluated by calculating Spearman’s rank correlation coefficient (rs) with the corresponding p-values. The gamma passing rates of 46 patients analyzed with the 2D/3D PerFRACTION™ using the 2%/2 mm and 3%/3 mm criteria showed almost similar trends to those analyzed with the Portal dose imaging prediction (PDIP) and ArcCHECK™ except for those analyzed with ArcCHECK™ using the 2%/2 mm criterion. Most of weak or moderate correlations between GPRs and plan complexity were observed for all QA systems. The trend of mean rs between GPRs using PDIP and 2D/3D PerFRACTION™ for both criteria and plan complexity indices as in the GPRs analysis was significantly similar for brain, prostate, and lung cases with lower complexity compared to H&N case. Furthermore, the trend of mean rs for 2D/3D PerFRACTION™ for H&N case with high complexity was similar to that of ArcCHECK™ and slightly lower correlation was observed than that of PDIP. This work showed that the performance of 2D/3D PerFRACTION™ for pretreatment patient-specific QA was almost comparable to that of PDIP, although there was small difference from ArcCHECK™ for some cases. Thus, we found that the PerFRACTION™ is a suitable QA system for pretreatment patient-specific QA in a variety of treatment sites.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. T.H. Kim, S.J. Oh, M.J. Kim et al., Prog. Med. Phys. 22, 61 (2011)

    ADS  Google Scholar 

  2. J.W. Lee, S. Hong, Y.L. Kim et al., Prog. Med. Phys. 17, 131 (2006)

    Google Scholar 

  3. B. Han, A. Ding, M. Lu et al., J. Appl. Clin. Med. Phys. 18, 9 (2017)

    Google Scholar 

  4. Y. Jeong, J.G. Oh, J.K. Kang et al., Radiat. Oncol. J. 38, 60 (2020)

    Article  Google Scholar 

  5. G. Moliner, L. Sorro, R. Verstraet et al., J. Appl. Clin. Med. Phys. 19, 133 (2018)

    Article  Google Scholar 

  6. D.S. Sharma, V. Mhatre, M. Heigrujam et al., J. Appl. Clin. Med. Phys. 11, 238 (2010)

    Article  Google Scholar 

  7. G. Li, Y. Zhang, X. Jiang et al., Phys. Med. 29, 295 (2013)

    Article  Google Scholar 

  8. C.Y. Lee, W.C. Kim, H.J. Kim et al., Prog. Med. Phys. 30, 120 (2019)

    Article  Google Scholar 

  9. A.H. Zhuang, A.J. Olch, J. Appl. Clin. Med. Phys. 19, 114 (2018)

    Article  Google Scholar 

  10. A.A. Sati, J. Figuredo, G.W. Jones et al., J. Med. Phys. 44, 16 (2019)

    Article  Google Scholar 

  11. L. Masi, R. Doro, V. Favuzza et al., Med. Phys. 40, 071718 (2013)

    Article  Google Scholar 

  12. D.A. Low, W.B. Harms, S. Mutic et al., Med. Phys 25, 656 (1998)

    Article  Google Scholar 

  13. S. Breciani, A.D. Dia, A. Maggio et al., Med. Phys. 40, 121711 (2013)

    Article  Google Scholar 

  14. J.M. Park, C.H. Choi, H.G. Wu et al., PLoS ONE 15, e0244690 (2020)

    Article  Google Scholar 

  15. J.D. Evans, Pacific grove (Brooks/Cole Pub. Co., California, 1996), p. Xxii

    Google Scholar 

  16. D.J. Benjamin, J.Q. Berger, M. Johannesson et al., Nat. Hum. Behav. 2, 6 (2018)

    Article  Google Scholar 

  17. A.J. Olch, K. O’Meara, K.K. Wong, Adv. Radiat. Oncol. 4, 722 (2019)

    Article  Google Scholar 

  18. D.L. Defoor, L.A. Vazquez-Quino, P. Mavroidis et al., J. Appl. Clin. Med. Phys. 16, 206 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (Ministry of Science and ICT, MSIT) (No. 2018R1D1A1B07049159 and 2020R1C1C100936611)

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jin-Beom Chung or Seonghee Kang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, SW., Lee, B., Song, C. et al. Clinical implementation of PerFRACTION™ for pre-treatment patient-specific quality assurance. J. Korean Phys. Soc. 80, 516–525 (2022). https://doi.org/10.1007/s40042-022-00440-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40042-022-00440-y

Keywords

Navigation