An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates. (7th January 2013)

• Record Type: Journal Article
• Authors: Torshabi, Ahmad Esmaili; Riboldi, Marco; Fooladi, Abbas Ali Imani; Mosalla, Seyed Mehdi Modarres; Baroni, Guido
• Is Part Of: Journal of applied clinical medical physics. Volume 14:Number 1(2013)
• Page Start: 102
• Page End: 114
• Publication Date: 2013-01-07
• Subjects: radiotherapy -- tumor tracking -- fuzzy logic -- data clustering -- adaptive prediction model; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1120/jacmp.v14i1.4008
• Languages: English
• ISSNs: 1526-9914

Abstract

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. PACS number: 87