Prediction of lung tumor motion extent through artificial neural network (ANN) using tumor size and location data

Abstract

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 cm2. 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.