Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy

Purpose To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. Methods The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left‐breast and rectal cancer patients who received radiation therapy. A two‐dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left‐breast treatment plans are re‐planned using the Pinnacle3 Auto‐Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re‐optimized treatment plans are compared with the original manually optimized plans. Results By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. Conclusions The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity‐modulated breast and rectal cancer radiation therapy.

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

[2]  H Keller,et al.  Controlling the dose distribution with gEUD-type constraints within the convex radiotherapy optimization framework , 2008, Physics in medicine and biology.

[3]  Sasa Mutic,et al.  Vision 20/20: Automation and advanced computing in clinical radiation oncology. , 2013, Medical physics.

[4]  M. Guckenberger,et al.  Evaluation of an automated knowledge based treatment planning system for head and neck , 2015, Radiation oncology.

[5]  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.

[6]  Joseph O Deasy,et al.  Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues. , 2010, International journal of radiation oncology, biology, physics.

[7]  Luca Cozzi,et al.  On the pre-clinical validation of a commercial model-based optimisation engine: application to volumetric modulated arc therapy for patients with lung or prostate cancer. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[8]  Kyle S. Cranmer.  Kernel estimation in high-energy physics , 2000 .

[9]  Carsten Brink,et al.  Automatic planning of head and neck treatment plans. , 2016, Journal of applied clinical medical physics.

[10]  Ben J M Heijmen,et al.  iCycle: Integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. , 2012, Medical physics.

[11]  Johanna Skarpman Munter,et al.  Dose-volume histogram prediction using density estimation , 2015, Physics in medicine and biology.

[12]  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.

[13]  Eugene Wong,et al.  Automated IMRT planning with regional optimization using planning scripts , 2013, Journal of applied clinical medical physics.

[14]  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.

[15]  Andrew Jackson,et al.  Evaluating inter-campus plan consistency using a knowledge based planning model. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

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

[17]  Yaorong Ge,et al.  Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: an intertechnique and interinstitutional study. , 2013, Medical physics.

[18]  Russell H. Taylor,et al.  Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study. , 2013, Medical physics.

[19]  F. Rademakers,et al.  ROOT — An object oriented data analysis framework , 1997 .

[20]  R Mohan,et al.  Algorithms and functionality of an intensity modulated radiotherapy optimization system. , 2000, Medical physics.

[21]  Xun Jia,et al.  A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. , 2014, Medical physics.

[22]  W. Ng,et al.  Comparison of Planning Quality and Efficiency Between Conventional and Knowledge-based Algorithms in Nasopharyngeal Cancer Patients Using Intensity Modulated Radiation Therapy. , 2015, International journal of radiation oncology, biology, physics.

[23]  P. Voet,et al.  Fully automated volumetric modulated arc therapy plan generation for prostate cancer patients. , 2014, International journal of radiation oncology, biology, physics.