Vol 27, No 1 (2022)
Research paper
Published online: 2022-01-27

open access

Page views 5244
Article views/downloads 411
Get Citation

Connect on Social Media

Connect on Social Media

Dosimetric accuracy of three dose calculation algorithms for radiation therapy of in situ non-small cell lung carcinoma

Manda Švabić Kolacio1, David Rajlić1, Milan Radojčić12, Ðeni Smilović Radojčić13, Nevena Obajdin1, Dea Dundara Debeljuh134, Slaven Jurković13
Rep Pract Oncol Radiother 2022;27(1):86-96.

Abstract

Background: Study determines differences in calculated dose distributions for non-small cell lung carcinoma (NSCLC) patients. NSCLC cases were investigated, being the most common lung cancer treated by radiotherapy in our clinical practice.

Materials and methods: A retrospective study of 15 NSCLC patient dose distributions originally calculated using standard superposition (SS) and recalculated using collapsed cone (CC) and Monte Carlo (MC) based algorithm expressed as dose to medium in medium (MCDm) and dose to water in medium (MCDw,) was performed so that prescribed dose covers at least 99% of the gross target volume (GTV).

Statistical analysis was performed for differences of conformity index (CI), heterogeneity index (HI), gradient index (GI), dose delivered to 2% of the volume (D2%), mean dose (Dmean) and percentage of volumes covered by prescribed dose (V70Gy). For organs at risk (OARs), Dmean and percentage of volume receiving 20 Gy and 5Gy (V20Gy, V5Gy) were analysed.

Results: Statistically significant difference for GTVs was observed between MCDw and SS algorithm in mean dose only. For planning target volumes (PTVs), statistically significant differences were observed in prescribed dose coverage for CC, MCDm and MCDw. The differences in mean CI value for the CC algorithm and mean HI value for MCDm and MCDw were statistically significant. There is a statistically significant difference in the number of MUs for MCDm and MCDw compared to SS.

Conclusion: All investigated algorithms succeed in managing the restrictive conditions of the clinical goals. This study shows the drawbacks of the CC algorithm compared to other algorithms used.

Article available in PDF format

View PDF Download PDF file

References

  1. Webb S, Nahum AE. A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. Phys Med Biol. 1993; 38(6): 653–666.
  2. Papanikolaou N, Battista JJ, Boyer AL. AAPM report 85: Tissue Inhomogeneity Corrections for Megavoltage Photon Beams. Report of the AAPM radiation therapy committee task group 65. Medical Physics Publishing, Madison 2004.
  3. Fotina I, Winkler P, Künzler T, et al. Advanced kernel methods vs. Monte Carlo-based dose calculation for high energy photon beams. Radiother Oncol. 2009; 93(3): 645–653.
  4. Grofsmid D, Dirkx M, Marijnissen H, et al. Dosimetric validation of a commercial Monte Carlo based IMRT planning system. Med Phys. 2010; 37(2): 540–549.
  5. Li J, Galvin J, Harrison A, et al. Dosimetric verification using monte carlo calculations for tissue heterogeneity-corrected conformal treatment plans following RTOG 0813 dosimetric criteria for lung cancer stereotactic body radiotherapy. Int J Radiat Oncol Biol Phys. 2012; 84(2): 508–513.
  6. Fotina I, Kragl G, Kroupa B, et al. Clinical comparison of dose calculation using the enhanced collapsed cone algorithm vs. a new Monte Carlo algorithm. Strahlenther Onkol. 2011; 187(7): 433–441.
  7. Takahashi W, Yamashita H, Saotome N, et al. Evaluation of heterogeneity dose distributions for Stereotactic Radiotherapy (SRT): comparison of commercially available Monte Carlo dose calculation with other algorithms. Radiat Oncol. 2012; 7: 20.
  8. Elcim Y, Dirican B, Yavas O. Dosimetric comparison of pencil beam and Monte Carlo algorithms in conformal lung radiotherapy. J Appl Clin Med Phys. 2018; 19(5): 616–624.
  9. Bosse C, Narayanasamy G, Saenz D, et al. Dose Calculation Comparisons between Three Modern Treatment Planning Systems. J Med Phys. 2020; 45(3): 143–147.
  10. Jurković S, Svabić M, Diklić A, et al. Reinforcing of QA/QC programs in radiotherapy departments in Croatia: results of treatment planning system verification. Med Dosim. 2013; 38(1): 100–104.
  11. IAEA Technical report series No. 430: Commissioning and quality assurance of computerized planning system for radiation treatment of cancer. International Atomic Energy Agency, Vienna 2004.
  12. Commissioning of radiotherapy treatment planning systems: Testing for typical external beam treatment techniques. International Atomic Energy Agency — TECDOC-1583, Vienna 2008.
  13. Giménez-Alventosa V, Antunes PCG, Vijande J, et al. Secondary particle production in tissue-like and shielding materials for light and heavy ions calculated with the Monte-Carlo code SHIELD-HIT. J Radiat Res. 2002; 43 Suppl(10): S93–S97.
  14. Radojčić ÐS, Kolacio MŠ, Radojčić M, et al. Comparison of calculated dose distributions reported as dose-to-water and dose-to-medium for intensity-modulated radiotherapy of nasopharyngeal cancer patients. Med Dosim. 2018; 43(4): 363–369.
  15. Wiesmeyer MD. A multigrid approach for accelerating three dimensional photon dose calculation. Med Phys . 1999; 26(1149 (Abstract)).
  16. Aarup LR, Nahum AE, Zacharatou C, et al. The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: implications for tumour coverage. Radiother Oncol. 2009; 91(3): 405–414.
  17. Elekta Monaco Dose Calculation Technical Reference (Crawley, Elekta) (IMPAC Medical Systems Inc. 2013.
  18. Fippel M. Fast Monte Carlo dose calculation for photon beams based on the VMC electron algorithm. Med Phys. 1999; 26(8): 1466–1475.
  19. Walters BRB, Kramer R, Kawrakow I. Dose to medium versus dose to water as an estimator of dose to sensitive skeletal tissue. Phys Med Biol. 2010; 55(16): 4535–4546.
  20. Ma CM, Li J. Dose specification for radiation therapy: dose to water or dose to medium? Phys Med Biol. 2011; 56(10): 3073–3089.
  21. Reynaert N, Crop F, Sterpin E, et al. On the conversion of dose to bone to dose to water in radiotherapy treatment planning systems. Phys Imaging Radiat Oncol. 2018; 5: 26–30.
  22. Kolacio M, Brkić H, Faj D, et al. Validation of two calculation options built in Elekta Monaco Monte Carlo based algorithm using MCNP code. Radiat Phys Chem. 2021; 179: 109237.
  23. Netherlands commission of radiation dosimetry. Code of practice for the quality assurance and control for intensity modulated radiotherapy; 2013.
  24. Venselaar J, Welleweerd H, Mijnheer B. Tolerances for the accuracy of photon beam dose calculations of treatment planning systems. Radiother Oncol. 2001; 60(2): 191–201.
  25. Radojcic DS, Casar B, Rajlic D, et al. Experimental validation of Monte Carlo based treatment planning system in bone density equivalent media. Radiol Oncol. 2020; 54(4): 495–504.
  26. Bradley JD, Bae K, Graham MV, et al. Primary analysis of the phase II component of a phase I/II dose intensification study using three-dimensional conformal radiation therapy and concurrent chemotherapy for patients with inoperable non-small-cell lung cancer: RTOG 0117. J Clin Oncol. 2010; 28(14): 2475–2480.
  27. Marks LB, Yorke ED, Jackson A, et al. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys. 2010; 76(3 Suppl): S10–S19.
  28. Marks LB, Bentzen SM, Deasy JO, et al. Radiation dose-volume effects in the lung. Int J Radiat Oncol Biol Phys. 2010; 76(3 Suppl): S70–S76.
  29. Bentzen SM, Constine LS, Deasy JO, et al. Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues. Int J Radiat Oncol Biol Phys. 2010; 76(3 Suppl): S3–S9.
  30. Riet A, Mak A, Moerland M, et al. A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: Application to the prostate. Int J Radiat Oncol Biol Phys. 1997; 37(3): 731–736.
  31. Feuvret L, Noël G, Mazeron JJ, et al. Conformity index: a review. Int J Radiat Oncol Biol Phys. 2006; 64(2): 333–342.
  32. Cao T, Dai Z, Ding Z, et al. Analysis of different evaluation indexes for prostate stereotactic body radiation therapy plans: conformity index, homogeneity index and gradient index. Precis Radiat Oncol. 2019; 3(3): 72–79.
  33. Krieger T, Sauer OA. Monte Carlo- versus pencil-beam-/collapsed-cone-dose calculation in a heterogeneous multi-layer phantom. Phys Med Biol. 2005; 50(5): 859–868.
  34. Sini C, Broggi S, Fiorino C, et al. Accuracy of dose calculation algorithms for static and rotational IMRT of lung cancer: A phantom study. Phys Med. 2015; 31(4): 382–390.
  35. Fogliata A, Cozzi L. Dose calculation algorithm accuracy for small fields in non-homogeneous media: The lung SBRT case. Phys Med. 2017; 44: 157–162.
  36. Lebredonchel S, Lacornerie T, Rault E, et al. About the non-consistency of PTV-based prescription in lung. Phys Med. 2017; 44: 177–187.
  37. Saito M, Suzuki H, Sano N, et al. Evaluation of the target dose coverage of stereotactic body radiotherapy for lung cancer using helical tomotherapy: A dynamic phantom study. Rep Pract Oncol Radiother. 2020; 25(2): 200–205.
  38. Machtay M, Bae K, Movsas B, et al. Higher biologically effective dose of radiotherapy is associated with improved outcomes for locally advanced non-small cell lung carcinoma treated with chemoradiation: an analysis of the Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys. 2012; 82(1): 425–434.
  39. Vanderstraeten B, Reynaert N, Paelinck L, et al. Accuracy of patient dose calculation for lung IMRT: A comparison of Monte Carlo, convolution/superposition, and pencil beam computations. Med Phys. 2006; 33(9): 3149–3158.



Reports of Practical Oncology and Radiotherapy