An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates

In the radiation treatment of moving targets with external surrogates, information on tumor position in real time can be extracted by using accurate correlation models. A fuzzy environment is proposed here to correlate input surrogate data with tumor motion estimates in real time. In this study, two different data clustering approaches were analyzed due to their substantial effects on the fuzzy modeler performance. Moreover, a comparative investigation was performed on two fuzzy‐based and one neuro‐fuzzy–based inference systems with respect to state‐of‐the‐art models. Finally, due to the intrinsic interpatient variability in fuzzy models' performance, a model selectivity algorithm was proposed to select an adaptive fuzzy modeler on a case‐by‐case basis. The performance of multiple and adaptive fuzzy logic models were retrospectively tested in 20 patients treated with CyberKnife real‐time tumor tracking. Final results show that activating adequate model selection of our fuzzy‐based modeler can significantly reduce tumor tracking errors. PACS number: 87

[1]  Lei Xing,et al.  Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression , 2009, Physics in medicine and biology.

[2]  Sheng-De Wang,et al.  Fuzzy system identification using an adaptive learning rule with terminal attractors , 1999, Fuzzy Sets Syst..

[3]  Hiroki Shirato,et al.  Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: a simulation study. , 2007, Medical physics.

[4]  K. Ohara,et al.  Irradiation synchronized with respiration gate. , 1989, International journal of radiation oncology, biology, physics.

[5]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[6]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

[7]  S Minohara,et al.  Respiratory gated irradiation system for heavy-ion radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  H. Kubo,et al.  Respiration gated radiotherapy treatment: a technical study. , 1996, Physics in medicine and biology.

[10]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[11]  Marco Riboldi,et al.  Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study , 2010, Technology in cancer research & treatment.

[12]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[13]  Marco Riboldi,et al.  Real-time tumour tracking in particle therapy: technological developments and future perspectives. , 2012, The Lancet. Oncology.

[14]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[16]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[17]  Shinichi Shimizu,et al.  Real-time tumour-tracking radiotherapy , 1999, The Lancet.

[18]  Lotfi A. Zadeh,et al.  Knowledge Representation in Fuzzy Logic , 1996, IEEE Trans. Knowl. Data Eng..

[19]  M. V. van Herk,et al.  Physical aspects of a real-time tumor-tracking system for gated radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[20]  Chun-Hee Woo,et al.  Evolutionary design of fuzzy rule base for nonlinear system modeling and control , 2000, IEEE Trans. Fuzzy Syst..

[21]  G Baroni,et al.  Robustness of external/internal correlation models for real-time tumor tracking to breathing motion variations , 2012, Physics in medicine and biology.

[22]  R Mohan,et al.  Predicting respiratory motion for four-dimensional radiotherapy. , 2004, Medical physics.

[23]  H Shirato,et al.  Inference of hysteretic respiratory tumor motion from external surrogates: a state augmentation approach , 2008, Physics in medicine and biology.

[24]  M. Hoogeman,et al.  Clinical accuracy of the respiratory tumor tracking system of the cyberknife: assessment by analysis of log files. , 2009, International journal of radiation oncology, biology, physics.

[25]  C. Ling,et al.  Technical aspects of the deep inspiration breath-hold technique in the treatment of thoracic cancer. , 2000, International journal of radiation oncology, biology, physics.

[26]  F. Ernst,et al.  Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter , 2007 .

[27]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[28]  Lotfi A. Zadeh,et al.  Fuzzy Algorithms , 1968, Inf. Control..

[29]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[30]  J. Fowler,et al.  CyberKnife radiosurgery for stage I lung cancer: results at 36 months. , 2007, Clinical lung cancer.

[31]  G. Sharp,et al.  Uncertainties in Lung Motion Prediction Relying on External Surrogate: A 4DCT Study in Regular vs. Irregular Breathers , 2010, Technology in cancer research & treatment.

[32]  G Baroni,et al.  Four-dimensional targeting error analysis in image-guided radiotherapy , 2009, Physics in medicine and biology.

[33]  G J Kutcher,et al.  Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. , 1999, International journal of radiation oncology, biology, physics.

[34]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[35]  Sonja Dieterich,et al.  Comparative performance of linear and nonlinear neural networks to predict irregular breathing , 2006, Physics in medicine and biology.

[36]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[37]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[38]  L. A. Zedeh.  Knowledge representation in fuzzy logic , 1989 .

[39]  Manish Kakar,et al.  Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). , 2005, Physics in medicine and biology.