Estimation of Radiobiological Indices in Radiotherapy of Lung Cancer using an Artificial Neural Network

The purpose of this study is to develop an artificial neural network (ANN) to predict radiobiological indices in radiotherapy of lung cancer. A total of 100 lung cancer patients’ treatment plans were selected for this study. The target outputs for ANN, Normal tissue complication probability (NTCP) of organs at risk (OAR), and tumor control probability (TCP) of the tumor were calculated. The inputs were planning target volume (PTV), treatment modality, location of the tumor, prescribed dose, number of fractions, the maximum dose to the tumor, and mean doses to the OARs. The ANN is based on a Scaled Conjugate gradient algorithm with one hidden layer having 11 inputs and 5 outputs. 70% of the data was used for training, and 30% for testing the ANN. The ANN predicts NTCP for OARs and TCP for the target with an overall regression value of 0.94. The fitted ANN has mean square error values 0.007 and 0.024 for training and testing respectively. The regression values are 0.97 and 0.91 for training and testing respectively. The results show that ANN can be designed to predict the radiobiological parameters within a 5% error as indicated by the regression value. To validate the full performance of the neural network in case of a lung cancer treatment plan, further research is in progress.