Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

Abstract

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

Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning

“…An alternative method is to train a convolutional neural network (CNN) on a large public dataset and perform transfer learning by fine-tuning the pre-trained model for target tracking in fluoroscopic images (17). Some studies have reported tumor motion estimation by external surface surrogate location using an optical monitoring system (18)(19)(20)(21)(22). However, it is difficult to construct a reliable correlation model between external surrogates and an internal tumor, and the model is subject to the changes of respiratory patterns (23).…”

Geometric and dosimetric comparison of four intrafraction motion adaptation strategies for stereotactic liver radiotherapy

“…The fiducial markers are used as tumor position surrogates to overcome the poor visibility of liver tumors in x-ray images. The second option uses the correlation between a continuous external surrogate signal and sparse x-ray monitoring of internal tumor motion (Nankali et al 2016), which is commercially available on specialized accelerators such as the Cyberknife (Schweikard et al 2000) and Vero (Kamino et al 2006), but also possible on conventional linear accelerators (Bertholet et al 2018). The third option is the use of electromagnetic transponders implanted near the tumor and monitored wirelessly by an external antenna system (Calypso, Varian Medical Systems, Palo Alto, CA) (Balter et al 2005).…”