A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

INTRODUCTION The quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. MATERIALS AND METHOD One hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. RESULTS A voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. CONCLUSION We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

[1]  Joseph O Deasy,et al.  CERR: a computational environment for radiotherapy research. , 2003, Medical physics.

[2]  Sasa Mutic,et al.  Predicting dose-volume histograms for organs-at-risk in IMRT planning. , 2012, Medical physics.

[3]  Binbin Wu,et al.  An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection. , 2012, Medical physics.

[4]  F. Lohr,et al.  Volumetric modulated arc therapy (VMAT) vs. serial tomotherapy, step-and-shoot IMRT and 3D-conformal RT for treatment of prostate cancer. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[5]  Fang-Fang Yin,et al.  Response to “Comment on ‘A planning quality evaluation tool for prostate adaptive IMRT based on machine learning’ ” [Med. Phys. 38, 719 (2011)]: Response to , 2011 .

[6]  Patricio Simari,et al.  Comment on "A planning quality evaluation tool for prostate adaptive IMRT based on machine learning" [Med. Phys. 38, 719 (2011)]. , 2011, Medical physics.

[7]  Russell H. Taylor,et al.  Patient geometry-driven information retrieval for IMRT treatment plan quality control. , 2009, Medical physics.

[8]  W. R. Lee,et al.  Knowledge-based IMRT treatment planning for prostate cancer. , 2011 .

[9]  Steven F Petit,et al.  Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[10]  J. Lo,et al.  A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. , 2013, International journal of radiation oncology, biology, physics.

[11]  Volker Steil,et al.  A fast radiotherapy paradigm for anal cancer with volumetric modulated arc therapy (VMAT) , 2009, Radiation oncology.

[12]  H. Gali-Muhtasib,et al.  The radiosensitizer 2-benzoyl-3-phenyl-6,7-dichloroquinoxaline 1,4-dioxide induces DNA damage in EMT-6 mammary carcinoma cells , 2009, Radiation oncology.

[13]  Y. Ge,et al.  Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans. , 2012, Medical physics.

[14]  F. Wenz,et al.  A virtual source model of a kilo-voltage radiotherapy device , 2013, Physics in medicine and biology.

[15]  Fang-Fang Yin,et al.  A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. , 2011, Medical physics.

[16]  Todd McNutt,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. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  Ping Xia,et al.  Can all centers plan intensity-modulated radiotherapy (IMRT) effectively? An external audit of dosimetric comparisons between three-dimensional conformal radiotherapy and IMRT for adjuvant chemoradiation for gastric cancer. , 2008, International journal of radiation oncology, biology, physics.