The Octavius Detector 1000 SRS is an accurate, precise, and reliable detector, very useful for the daily performance of the patient specific quality assurance of radiotherapy treatment plans.

The aim of this study is to assess the possibility of developing novel predictive models based on data mining algorithms which would provide an automatic tool for the calculation of the extent of lung tumor motion characterized by its known location and size. Data mining is an analytic process designed to explore data in search of regular patterns and relationships between variables. The ultimate goal of data mining is prediction of the future behavior. Artificial neural network (ANN) data-mining algorithm was used to develop an automatic model, which was trained to predict extent of the tumor motion using the data set obtained from the available 4D CT imaging data. The accuracy of the designed neural network was tested by using longer training time, different input values and/or more neurons in its hidden layer. An optimized ANN best fit the training and test datasets with a regression value (R) of 0.97 and mean squared error value of 0.0039 cm 2. The maximum error that was recorded for the best network performance was 0.32 cm in the craniocaudal direction. The overall prediction error was largest in this direction for 70% of the studied cases. In this study, the concepts of neural networks were discussed and an ANN algorithm is proposed to be used with clinical lung tumor information for the prediction of the tumor motion extent. The results of optimized ANN are promising and can be a reliable tool for motion pattern calculation. It is an automated tool, which may assist radiation oncologists in defining the tumor margins needed in lung cancer radiation therapy.

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The purpose of the study was to assess internal target volume changes through the breathing cycle and associated tumour motion for lung patients and to establish possible correlations between different parameters. Respiration-induced volume changes with breathing cycle and the associated tumour motion were analyzed for 11 patients. Selected phases were the maximum and average intensity projections and the 10 phases of equal duration and separation obtained through the respiratory cycle. Tumour centre of mass (COM) motion planes were generated using least square fitting, and their angles and orientations were then compared between the cases studied. Trajectories that are composed by the points of COM location in different phases were identified, and their interrelation was assessed through different similarity measures. The results were used to determine if there is any correlation between parameters chosen and if the margins conventionally used for the planning target volume creation successfully encompassed lung tumour motion and volume change. The results show that the extent of tumour motion was related to its volume and location. The tumour displacement was predominantly left

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