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Development of 3D biological effective dose distribution software program

Published online by Cambridge University Press:  09 September 2016

Patchareewan Khadsiri ,
Ekkasit Tharavichitkul ,
Suwit Saekho  and
Nisa Chawapun
Patchareewan Khadsiri
Affiliation:
Medical Physics Master Degree Program, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
Ekkasit Tharavichitkul*
Affiliation:
The Division of Therapeutic Radiology and Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
Suwit Saekho
Affiliation:
The Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
Nisa Chawapun
Affiliation:
The Division of Therapeutic Radiology and Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
*
Correspondence to: Ekkasit Tharavichitkul, The Division of Therapeutic Radiology and Oncology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand. E-mail: paan_31@hotmail.com
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Abstract

Purpose

To develop a software program to convert physical dose distribution into biological effective dose (BED).

Methods

The MATLAB-based BED distribution software program was designed to import the radiotherapy treatment plan from the computer treatment planning system and to convert the physical dose distribution into the BED distribution. The BED calculation was based on the linear-quadratic-linear model (LQ-L model). Besides radiobiological parameters, other specific data could be fed in through the panel. The accuracy of the program was verified by comparing the BED distribution with manual calculation.

Results

This software program was able to import the radiotherapy treatment plans and pull out pixel-wised physical dose for BED calculation, and display the isoBED lines on the computed tomographic (CT) image. The verification of BED dose distribution was performed in both phantom and clinical cases. It revealed that there were no differences between the program and manual BED calculations.

Conclusion

It is feasible and practical to use this in-house BED distribution software program in clinical practices and research work. However, it should be used with caution as the validity of the program depends on the accuracy of the published biological parameters.

Keywords

biological effective dosedose distributionlinear-quadratic-linear modelsoftware program
Type
Original Articles
Information
Journal of Radiotherapy in Practice , Volume 16 , Issue 4 , December 2017 , pp. 383 - 390
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Copyright
© Cambridge University Press 2016 

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References

1. Barendsen, GW. Dose fractionation, dose rate and isoeffect relationships for normal tissue responses. Int J Radiat Oncol Biol Phys 1982; 8: 197–198.CrossRefGoogle ScholarPubMed
2. Juan, CLA. Modelling and optimization in radiotherapy. Master Degree Thesis. Spain: Complutense University of Madrid, Madrid, 2010.Google Scholar
3. Jacobs, P, Nelson, A, Liu, L. Biological effective dose and tumor control probability modeling using the MIM® Software Suite. MIM Software Inc, Cleveland, 2012:1-3.Google Scholar
4. Lee, SP, Leu, MY, Smathers, JB, Mcbride, WH, Parker, RG, Withers, HR. Biologically effective dose distribution based on the linear quadratic model and its clinical relevance. Int J Radiat Oncol Biol Phys 1995; 33: 375–389.CrossRefGoogle ScholarPubMed
5. Joiner, M, Kogel, AVD. Basic Clinical Radiobiology, 4th edition. Great Britain: Great Britain Hodder Arnold, 2009: 191–204.CrossRefGoogle Scholar
6. Hall, EJ, Giacca, AJ. Radiobiology for the Radiologist, 6th edition. Philadelphia: Lippincott Williams & Wilkins, 2006.Google Scholar
7. Bruzzaniti, V, Abate, A, Pedrini, M, Benassi, M, Strigari, L. IsoBED: a tool for automatic calculation of biologically equivalent fractionation schedules in radiotherapy using IMRT with a Simultaneous Integrated Boost (SIB) Technique. J Exp Clin Cancer Res 2011; 30: 52.CrossRefGoogle ScholarPubMed
8. Voyant, C, Julian, D, Roustit, R, Biffi, K, Lantieri, C. Biological effects and equivalent dose in radiotherapy: a software solution. Rep Pract Oncol Radiother 2014; 19: 47–55.CrossRefGoogle ScholarPubMed
9. Hedman, M, Bjork-Eriksson, T, Brodin, O, Dasu, T. Predictive value of modeled tumour control probability based on individual measurements of in vitro radiosensitivity and potential doubling time. Br J Radiol 2013; 86: 1–10.CrossRefGoogle ScholarPubMed
10. Kehwar, TS. Analytical approach to estimate normal tissue complication probability using best fit of normal tissue tolerance doses into the NTCP equation of the linear quadratic model. J Cancer Res Ther 2005; 1: 168–179.CrossRefGoogle ScholarPubMed
11. Deasy, JO, Blanco, AI, Clark, VH. CERR: a Computational Environment For Radiotherapy Research. Med Phys 2003; 30: 979–985.CrossRefGoogle ScholarPubMed
12. Davenport, DA. Development of a quality assurance procedure for dose volume histogram analysis. Master of Science in Biomedical Sciences Degree. Toledo, OH: The University of Toledo, 2013.Google Scholar
13. Allen, LX, Alber, M, O. Deasy, J et al. The use and QA of biologically related models for treatment planning. Med Phys 2012; 166: 1–60.Google Scholar