title: An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates.
abstract: 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.
authors: Department of Electrical and Computer Engineering, Kerman Graduate University of Technology, Kerman, Iran.
authors: Ahmad
authors: A
authors: Esmaili Torshabi
authors: Marco
authors: M
authors: Riboldi
authors: Abbas Ali
authors: AA
authors: Imani Fooladi
authors: Seyed Mehdi
authors: SM
authors: Modarres Mosalla
authors: Guido
authors: G
authors: Baroni
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000532:radiotherapy*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity