期刊论文详细信息
Radiation Oncology
Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning
Gerhard Glatting2  Frederik Wenz1  Dwi Seno Kuncoro Sihono1  Hana Mekdash1  Obioma Nwankwo2 
[1] Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany;Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
关键词: Normal tissue sparing;    Treatment plan optimization;    Dose prediction algorithm;    Personalized radiotherapy treatment planning;    Knowledge-based radiation therapy (KBRT) treatment planning;   
Others  :  1226191
DOI  :  10.1186/s13014-015-0416-6
 received in 2015-04-20, accepted in 2015-04-30,  发布年份 2015
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【 摘 要 】

Background

A knowledge-based radiation therapy (KBRT) treatment planning algorithm was recently developed. The purpose of this work is to investigate how plans that are generated with the objective KBRT approach compare to those that rely on the judgment of the experienced planner.

Methods

Thirty volumetric modulated arc therapy plans were randomly selected from a database of prostate plans that were generated by experienced planners (expert plans). The anatomical data (CT scan and delineation of organs) of these patients and the KBRT algorithm were given to a novice with no prior treatment planning experience. The inexperienced planner used the knowledge-based algorithm to predict the dose that the OARs receive based on their proximity to the treated volume. The population-based OAR constraints were changed to the predicted doses. A KBRT plan was subsequently generated. The KBRT and expert plans were compared for the achieved target coverage and OAR sparing. The target coverages were compared using the Uniformity Index (UI), while 5 dose-volume points (D10, D30, D50, D70 and D90) were used to compare the OARs (bladder and rectum) doses. Wilcoxon matched-pairs signed rank test was used to check for significant differences (p < 0.05) between both datasets.

Results

The KBRT and expert plans achieved mean UI values of 1.10 ± 0.03 and 1.10 ± 0.04, respectively. The Wilcoxon test showed no statistically significant difference between both results. The D90, D70, D50, D30 and D10 values of the two planning strategies, and the Wilcoxon test results suggests that the KBRT plans achieved a statistically significant lower bladder dose (at D30), while the expert plans achieved a statistically significant lower rectal dose (at D10 and D30).

Conclusions

The results of this study show that the KBRT treatment planning approach is a promising method to objectively incorporate patient anatomical variations in radiotherapy treatment planning.

【 授权许可】

   
2015 Nwankwo et al.; licensee BioMed Central.

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【 参考文献 】
  • [1]Good D, Lo J, Lee WR, Wu QJ, Yin F-F, Das SK. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. Int J Radiat Oncol, Biol, Phys. 2013; 87(1):176-181.
  • [2]Nwankwo O, Sihono DSK, Schneider F, Wenz F. A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites). Phys Med Biol. 2014; 59(18):5575-5591.
  • [3]Wang Y, Zolnay A, Incrocci L, Joosten H, McNutt T, Heijmen B et al.. A quality control model that uses PTV-rectal distances to predict the lowest achievable rectum dose, improves IMRT planning for patients with prostate cancer. Radiother Oncol. 2013; 107(3):352-357.
  • [4]Appenzoller LM, Michalski JM, Thorstad WL, Mutic S, Moore KL. Predicting dose-volume histograms for organs-at-risk in IMRT planning. Med Phys. 2012; 39(12):7446.
  • [5]Yuan L, Ge Y, Lee WR, Yin FF, Kirkpatrick JP, Wu QJ. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. Med Phys. 2012; 39:6868.
  • [6]Petit SF, Wu B, Kazhdan M, Dekker A, Simari P, Kumar R et al.. Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma. Radiother Oncol. 2012; 102(1):38-44.
  • [7]Zhu X, Ge Y, Li T, Thongphiew D, Yin F-F, Wu QJ. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys. 2011; 38:719.
  • [8]Wu B, Ricchetti F, Sanguineti G, Kazhdan M, Simari P, Chuang M et al.. Patient geometry-driven information retrieval for IMRT treatment plan quality control. Med Phys. 2009; 36:5497.
  • [9]Sharpe MB, Moore KL, Orton CG. Within the next ten years treatment planning will become fully automated without the need for human intervention. Med Phys. 2014; 41(12):120601.
  • [10]McIntosh A, Read PW, Khandelwal SR, Arthur DW, Turner AB, Ruchala KJ et al.. Evaluation of coplanar partial left breast irradiation using tomotherapy-based topotherapy. Int J Radiat Oncol, Biol, Phys. 2008; 71(2):603-610.
  • [11]Wang X, Zhang X, Dong L, Liu H, Gillin M, Ahamad A et al.. Effectiveness of noncoplanar IMRT planning using a parallelized multiresolution beam angle optimization method for paranasal sinus carcinoma. Int J Radiat Oncol, Biol, Phys. 2005; 63(2):594-601.
  • [12]Chanyavanich V, Das SK, Lee WR, Lo JY. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011; 38:2515.
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