A fuzzy system for evaluating radiation treatment plans of head and neck cancer

In this study, 14 treatment planning dose parameters for head and neck cancer are adopted as the characteristic functions to establish the statistical analysis of the fuzzy system module for radiation treatment plan classification. By constructing fuzzy rules and fuzzy set membership functions, we aim to improve the classification efficiency and accuracy by the fuzzy systems, and to improve the quality of the treatment plan. Three different fuzzy logic control systems were used for analysis of 100 Pinnacle treatment planning system datasets. The results show that the accuracy may be up to 100%. The fuzzy logic control system we propose may be a useful tool for accurate planning and decision-making.

[1]  Qiang Shen,et al.  Fuzzy interpolative reasoning via scale and move transformations , 2006, IEEE Transactions on Fuzzy Systems.

[2]  S. Webb,et al.  Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate. , 2004, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[3]  Fu-Min Fang,et al.  Dosimetric comparisons of helical tomotherapy and step-and-shoot intensity-modulated radiotherapy in nasopharyngeal carcinoma. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  Ester Bernadó-Mansilla,et al.  Fuzzy-UCS: A Michigan-Style Learning Fuzzy-Classifier System for Supervised Learning , 2009, IEEE Transactions on Evolutionary Computation.

[5]  Pierluigi Siano,et al.  A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control , 2010, IEEE Transactions on Industrial Electronics.

[6]  Hannu Koivisto,et al.  Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms , 2008, Int. J. Approx. Reason..

[7]  Adisorn Thomya,et al.  Design of Control System of Hydrogen and Oxygen Flow Rate for Proton Exchange Membrane Fuel Cell Using Fuzzy Logic Controller , 2011 .

[8]  Hai Jin,et al.  Object segmentation using ant colony optimization algorithm and fuzzy entropy , 2007, Pattern Recognit. Lett..

[9]  Musa H. Asyali,et al.  Nonlinear system identification via Laguerre network based fuzzy systems , 2009, Fuzzy Sets Syst..

[10]  Tsair-Fwu Lee,et al.  Applications of Artificial Neural Network to Building Statistical Models for Qualifying and Indexing Radiation Treatment Plans , 2010 .

[11]  Shi-Long Lian,et al.  Dosimetric comparison of helical tomotherapy and dynamic conformal arc therapy in stereotactic radiosurgery for vestibular schwannomas. , 2011, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[12]  Churn-Jung Liau,et al.  Fuzzy Interpolative Reasoning for Sparse Fuzzy-Rule-Based Systems Based on the Areas of Fuzzy Sets , 2008, IEEE Transactions on Fuzzy Systems.