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A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.

Publication ,  Journal Article
Zhu, X; Ge, Y; Li, T; Thongphiew, D; Yin, F-F; Wu, QJ
Published in: Med Phys
February 2011

PURPOSE: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. METHODS: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. RESULTS: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy approximately 80% in prediction and effectiveness in improving ART planning quality. CONCLUSIONS: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

Duke Scholars

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Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

February 2011

Volume

38

Issue

2

Start / End Page

719 / 726

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Quality Control
  • Prostatic Neoplasms
  • Principal Component Analysis
  • Organ Size
  • Nuclear Medicine & Medical Imaging
  • Male
 

Citation

APA
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ICMJE
MLA
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Zhu, X., Ge, Y., Li, T., Thongphiew, D., Yin, F.-F., & Wu, Q. J. (2011). A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Med Phys, 38(2), 719–726. https://doi.org/10.1118/1.3539749
Zhu, Xiaofeng, Yaorong Ge, Taoran Li, Danthai Thongphiew, Fang-Fang Yin, and Q Jackie Wu. “A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.Med Phys 38, no. 2 (February 2011): 719–26. https://doi.org/10.1118/1.3539749.
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 Feb;38(2):719–26.
Zhu, Xiaofeng, et al. “A planning quality evaluation tool for prostate adaptive IMRT based on machine learning.Med Phys, vol. 38, no. 2, Feb. 2011, pp. 719–26. Pubmed, doi:10.1118/1.3539749.
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 Feb;38(2):719–726.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

February 2011

Volume

38

Issue

2

Start / End Page

719 / 726

Location

United States

Related Subject Headings

  • Retrospective Studies
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Quality Control
  • Prostatic Neoplasms
  • Principal Component Analysis
  • Organ Size
  • Nuclear Medicine & Medical Imaging
  • Male