title: Development of a Synthetic Adaptive Neuro-Fuzzy Prediction Model for Tumor Motion Tracking in External Radiotherapy by Evaluating Various Data Clustering Algorithms.
abstract: In image guided radiotherapy, in order to reach a prescribed uniform dose in dynamic tumors at thorax region while minimizing the amount of additional dose received by the surrounding healthy tissues, tumor motion must be tracked in real-time. Several correlation models have been proposed in recent years to provide tumor position information as a function of time in radiotherapy with external surrogates. However, developing an accurate correlation model is still a challenge. In this study, we proposed an adaptive neuro-fuzzy based correlation model that employs several data clustering algorithms for antecedent parameters construction to avoid over-fitting and to achieve an appropriate performance in tumor motion tracking compared with the conventional models. To begin, a comparative assessment is done between seven nuero-fuzzy correlation models each constructed using a unique data clustering algorithm. Then, each of the constructed models are combined within an adaptive sevenfold synthetic model since our tumor motion database has high degrees of variability and that each model has its intrinsic properties at motion tracking. In the proposed sevenfold synthetic model, best model is selected adaptively at pre-treatment. The model also updates the steps for each patient using an automatic model selectivity subroutine. We tested the efficacy of the proposed synthetic model on twenty patients (divided equally into two control and worst groups) treated with CyberKnife synchrony system. Compared to Cyberknife model, the proposed synthetic model resulted in 61.2% and 49.3% reduction in tumor tracking error in worst and control group, respectively. These results suggest that the proposed model selection program in our synthetic neuro-fuzzy model can significantly reduce tumor tracking errors. Numerical assessments confirmed that the proposed synthetic model is able to track tumor motion in real time with high accuracy during treatment.
authors.affiliation: Department of Electrical and Computer Engineering, Medical Radiation Group, Graduate University of Advanced Technology, Haft Bagh-e-Alavi Highway, Mahan Knowledge Paradise, Kerman, Iran.
authors.forename: Leila
authors.initials: L
authors.lastname: Ghorbanzadeh
authors.affiliation: Department of Electrical and Computer Engineering, Medical Radiation Group, Graduate University of Advanced Technology, Haft Bagh-e-Alavi Highway, Mahan Knowledge Paradise, Kerman, Iran a.esmaili@kgut.ac.ir ahmad4958@gmail.com.
authors.forename: Ahmad Esmaili
authors.initials: AE
authors.lastname: Torshabi
authors.affiliation: Department of Electrical and Computer Engineering, Medical Radiation Group, Graduate University of Advanced Technology, Haft Bagh-e-Alavi Highway, Mahan Knowledge Paradise, Kerman, Iran.
authors.forename: Jamshid Soltani
authors.initials: JS
authors.lastname: Nabipour
authors.affiliation: Department of Electrical and Computer Engineering, Medical Radiation Group, Graduate University of Advanced Technology, Haft Bagh-e-Alavi Highway, Mahan Knowledge Paradise, Kerman, Iran.
authors.forename: Moslem Ahmadi
authors.initials: MA
authors.lastname: Arbatan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D008962:Models, Theoretical; D009068:Movement; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D016634:Radiosurgery; D011881:Radiotherapy, Computer-Assisted; D061089:Radiotherapy, Image-Guided / Q000379:methods*
keywords: data clustering; radiotherapy; synthetic ANFIS; tumor motion tracking