Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study
| Autor: | Payam Samadi Miandoab, Saber Nankali, Ahmad Esmaili Torshabi, Amin Baghizadeh |
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| Rok vydání: | 2016 |
| Předmět: |
external markers
Mean squared error Computer science Movement Feature selection Models Biological Imaging phantom 030218 nuclear medicine & medical imaging Correlation 03 medical and health sciences 0302 clinical medicine optimum location Fiducial Markers Humans Radiation Oncology Physics Radiology Nuclear Medicine and imaging Instrumentation Statistical hypothesis testing Adaptive neuro fuzzy inference system Radiation Phantoms Imaging Respiration Liver Neoplasms external‐beam radiotherapy tumor tracking feature selection algorithms 030220 oncology & carcinogenesis Principal component analysis Fiducial marker Algorithm Algorithms |
| Zdroj: | Scopus-Elsevier Journal of Applied Clinical Medical Physics |
| ISSN: | 1526-9914 |
| Popis: | In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc |
| Databáze: | OpenAIRE |
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