Drug Resistance Prediction of Non-small Cell Lung Cancer Based on Deep Learning

摘要

Non-small cell lung cancer (NSCLC) is one of the common lung cancers and mainly treated clinically through EGFR-TKI. As time goes on, NSCLC patients are prone to develop resistance to the drug. The resistance may be different types, which increases the difficulties of clinical treatment. In order to capture the biological characteristics of tumor at different timepoints, we analyzed the serial medical images of NSCLC patients, and proposed a neural network using Convolutional Neural Network (CNN) and Recurrent neural networks (RNN), to predict the type of drug resistance in NSCLC patients. The CNN is used to extract the features of tumor images at different timepoints, the RNN will then analyze these features and take the time domain into account. A total of 168 NSCLC patients are split 3:1 into training cohort and test cohort, which are used to train and evaluate the model. In the end, the model achieved 79.16% accuracy on the test set. The results show that the method has obtain preliminary effect in the prediction of drug resistance in NSCLC patients.