Radiation Oncology Journal

  • 제30권1호
  • /
  • Pages.43-48
  • /
  • 2012
  • /
  • 2234-1900(pISSN)
  • /
  • 2234-3156(eISSN)
대한방사선종양학회 (The Korean Society for Radiation Oncology)
권호 표지
DOI QR코드

DOI QR Code

Development of the DVH management software for the biologically-guided evaluation of radiotherapy plan

  • Kim, Bo-Kyong (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Park, Hee-Chul (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Oh, Dong-Ryul (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Shin, Eun-Hyuk (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Ahn, Yong-Chan (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Kim, Jin-Sung (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Han, Young-Yih (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine)
  • 투고 : 2012.03.05
  • 심사 : 2012.03.26
  • 발행 : 2012.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Purpose: To develop the dose volume histogram (DVH) management software which guides the evaluation of radiotherapy (RT) plan of a new case according to the biological consequences of the DVHs from the previously treated patients. Materials and Methods: We determined the radiation pneumonitis (RP) as an biological response parameter in order to develop DVH management software. We retrospectively reviewed the medical records of lung cancer patients treated with curative 3-dimensional conformal radiation therapy (3D-CRT). The biological event was defined as RP of the Radiation Therapy Oncology Group (RTOG) grade III or more. Results: The DVH management software consisted of three parts (pre-existing DVH database, graphical tool, and $Pinnacle^3$ script). The pre-existing DVH data were retrieved from 128 patients. RP events were tagged to the specific DVH data through retrospective review of patients' medical records. The graphical tool was developed to present the complication histogram derived from the preexisting database (DVH and RP) and was implemented into the radiation treatment planning (RTP) system, $Pinnacle^3$ v8.0 (Phillips Healthcare). The software was designed for the pre-existing database to be updated easily by tagging the specific DVH data with the new incidence of RP events at the time of patients' follow-up. Conclusion: We developed the DVH management software as an effective tool to incorporate the phenomenological consequences derived from the pre-existing database in the evaluation of a new RT plan. It can be used not only for lung cancer patients but also for the other disease site with different toxicity parameters.

키워드

참고문헌

  1. Lyman JT. Complication probability as assessed from dosevolume histograms. Radiat Res Suppl 1985;8:S13-9. https://doi.org/10.2307/3583506
  2. Kutcher GJ, Burman C. Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. Int J Radiat Oncol Biol Phys 1989;16:1623-30. https://doi.org/10.1016/0360-3016(89)90972-3
  3. Kallman P, Agren A, Brahme A. Tumour and normal tissue responses to fractionated non-uniform dose delivery. Int J Radiat Biol 1992;62:249-62. https://doi.org/10.1080/09553009214552071
  4. Niemierko A. Reporting and analyzing dose distributions: a concept of equivalent uniform dose. Med Phys 1997;24:103-10. https://doi.org/10.1118/1.598063
  5. Oh D, Ahn YC, Park HC, Lim DH, Han Y. Prediction of radiation pneumonitis following high-dose thoracic radiation therapy by 3 Gy/fraction for non-small cell lung cancer: analysis of clinical and dosimetric factors. Jpn J Clin Oncol 2009;39:151-7. https://doi.org/10.1093/jjco/hyn158
  6. Jackson A, Yorke ED, Rosenzweig KE. The atlas of complication incidence: a proposal for a new standard for reporting the results of radiotherapy protocols. Semin Radiat Oncol 2006;16:260-8. https://doi.org/10.1016/j.semradonc.2006.04.009
  7. Moon SH, Shin KH, Kim TH, et al. Dosimetric comparison of four different external beam partial breast irradiation techniques: three-dimensional conformal radiotherapy, intensity-modulated radiotherapy, helical tomotherapy, and proton beam therapy. Radiother Oncol 2009;90:66-73. https://doi.org/10.1016/j.radonc.2008.09.027
  8. Chen YJ, Liu A, Han C, et al. Helical tomotherapy for radiotherapy in esophageal cancer: a preferred plan with better conformal target coverage and more homogeneous dose distribution. Med Dosim 2007;32:166-71. https://doi.org/10.1016/j.meddos.2006.12.003
  9. Fenkell L, Kaminsky I, Breen S, Huang S, Van Prooijen M, Ringash J. Dosimetric comparison of IMRT vs. 3D conformal radiotherapy in the treatment of cancer of the cervical esophagus. Radiother Oncol 2008;89:287-91. https://doi.org/10.1016/j.radonc.2008.08.008

피인용 문헌

  1. Two non-parametric methods for derivation of constraints from radiotherapy dose–histogram data vol.59, pp.13, 2014, https://doi.org/10.1088/0031-9155/59/13/n101
  2. Derivation and representation of dose-volume response from large clinical trial data sets: an example from the RADAR prostate radiotherapy trial vol.489, pp.1, 2012, https://doi.org/10.1088/1742-6596/489/1/012090
  3. A simple DVH generation technique for various radiotherapy treatment planning systems for an independent information system vol.67, pp.1, 2012, https://doi.org/10.3938/jkps.67.254
  4. Treatment outcome of hepatic re-irradiation in patients with hepatocellular carcinoma vol.33, pp.4, 2015, https://doi.org/10.3857/roj.2015.33.4.276