Feasibility Study of Deep Learning Based Radiation Sensitivity Prediction Model Using Gene Expression Profiling
Abstract Since radiation sensitivity prediction can be used in various field, we investigate the feasibility of an in vitro radiation sensitivity prediction model using a deep neural network. A microarray of the National Cancer Institute-60 tumor cell lines and clonogenic surviving fraction at an absorbed dose of 2 Gy values are used to predict radiation sensitivity. The prediction model is based on convolutional neural network and 6-fold cross-validation approach is applied to validate the model. Of the 174 samples, 170 (97.7%) samples show less than 10% and 4 (2.30%) show more than 10% of relative error, respectively. Through an additional validation, model accurately predict 172 out of 174 samples, representing a prediction accuracy of 98.85% under the criteria of absolute error < 0.01 or the relative error < 10%. This results demonstrate that in vitro radiation sensitivity prediction from gene expression can be carried out with the deep learning technology.