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Utilizing knowledge from prior plans in the evaluation of quality assurance.

Publication ,  Journal Article
Stanhope, C; Wu, QJ; Yuan, L; Liu, J; Hood, R; Yin, F-F; Adamson, J
Published in: Phys Med Biol
June 21, 2015

Increased interest regarding sensitivity of pre-treatment intensity modulated radiotherapy and volumetric modulated arc radiotherapy (VMAT) quality assurance (QA) to delivery errors has led to the development of dose-volume histogram (DVH) based analysis. This paradigm shift necessitates a change in the acceptance criteria and action tolerance for QA. Here we present a knowledge based technique to objectively quantify degradations in DVH for prostate radiotherapy. Using machine learning, organ-at-risk (OAR) DVHs from a population of 198 prior patients' plans were adapted to a test patient's anatomy to establish patient-specific DVH ranges. This technique was applied to single arc prostate VMAT plans to evaluate various simulated delivery errors: systematic single leaf offsets, systematic leaf bank offsets, random normally distributed leaf fluctuations, systematic lag in gantry angle of the mutli-leaf collimators (MLCs), fluctuations in dose rate, and delivery of each VMAT arc with a constant rather than variable dose rate.Quantitative Analyses of Normal Tissue Effects in the Clinic suggests V75Gy dose limits of 15% for the rectum and 25% for the bladder, however the knowledge based constraints were more stringent: 8.48 ± 2.65% for the rectum and 4.90 ± 1.98% for the bladder. 19 ± 10 mm single leaf and 1.9 ± 0.7 mm single bank offsets resulted in rectum DVHs worse than 97.7% (2σ) of clinically accepted plans. PTV degradations fell outside of the acceptable range for 0.6 ± 0.3 mm leaf offsets, 0.11 ± 0.06 mm bank offsets, 0.6 ± 1.3 mm of random noise, and 1.0 ± 0.7° of gantry-MLC lag.Utilizing a training set comprised of prior treatment plans, machine learning is used to predict a range of achievable DVHs for the test patient's anatomy. Consequently, degradations leading to statistical outliers may be identified. A knowledge based QA evaluation enables customized QA criteria per treatment site, institution and/or physician and can often be more sensitive to errors than criteria based on organ complication rates.

Duke Scholars

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

June 21, 2015

Volume

60

Issue

12

Start / End Page

4873 / 4891

Location

England

Related Subject Headings

  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Quality Control
  • Quality Assurance, Health Care
  • Prostatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Male
  • Humans
  • Algorithms
 

Citation

APA
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ICMJE
MLA
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Stanhope, C., Wu, Q. J., Yuan, L., Liu, J., Hood, R., Yin, F.-F., & Adamson, J. (2015). Utilizing knowledge from prior plans in the evaluation of quality assurance. Phys Med Biol, 60(12), 4873–4891. https://doi.org/10.1088/0031-9155/60/12/4873
Stanhope, Carl, Q Jackie Wu, Lulin Yuan, Jianfei Liu, Rodney Hood, Fang-Fang Yin, and Justus Adamson. “Utilizing knowledge from prior plans in the evaluation of quality assurance.Phys Med Biol 60, no. 12 (June 21, 2015): 4873–91. https://doi.org/10.1088/0031-9155/60/12/4873.
Stanhope C, Wu QJ, Yuan L, Liu J, Hood R, Yin F-F, et al. Utilizing knowledge from prior plans in the evaluation of quality assurance. Phys Med Biol. 2015 Jun 21;60(12):4873–91.
Stanhope, Carl, et al. “Utilizing knowledge from prior plans in the evaluation of quality assurance.Phys Med Biol, vol. 60, no. 12, June 2015, pp. 4873–91. Pubmed, doi:10.1088/0031-9155/60/12/4873.
Stanhope C, Wu QJ, Yuan L, Liu J, Hood R, Yin F-F, Adamson J. Utilizing knowledge from prior plans in the evaluation of quality assurance. Phys Med Biol. 2015 Jun 21;60(12):4873–4891.
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

June 21, 2015

Volume

60

Issue

12

Start / End Page

4873 / 4891

Location

England

Related Subject Headings

  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Quality Control
  • Quality Assurance, Health Care
  • Prostatic Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Male
  • Humans
  • Algorithms