PET-Based Deep-Learning Model for Predicting Prognosis of Patients With Non-Small Cell Lung Cancer

Abstract

Despite recent advances in precision medicine, lung cancer remains the leading cause of cancer-related mortality worldwide. To determine the prognosis of non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer, comprehensive analysis of various clinical factors are necessary. Artificial intelligence can help physician quickly identify key information from the vast amount of medical information including positron emission tomography (PET) scan. In this study, we compared image feature-extraction models and survival estimation models to determine an optimal model that effectively extracts features related to survival time. We collected PET image data of 2,685 patients who were diagnosed with NSCLC and received treatment at the Chonnam National University Hwasun Hospital in South Korea over a period of seven years. We compared four convolution neural network models, DenseNet, NFNet, EfficientNet, and ResNet, and two survival estimation models, CoxPH and CoxCC. The best model was determined based on criteria such as C-index, mean absolute error (MAE), classification accuracy for survival status, and learning time. The results show that DenseNet combined with CoxPH delivers superior performance for most of the criteria. In particular, the MAE for this combination was very low (391.50 days), and the model predicted survival days well; the five-year classification accuracy, which can indicate a cure for cancer, was high (95%). Extracted features were visualized using Score-CAM; thus, the learning process of the model could be understood without requiring expert knowledge of PET. In addition, the learning time for this model was short.

Keywords

• Convolutional neural network
• CoxPH
• CoxCC
• non-small cell lung cancer prognosis
• Score-CAM
• positron emission tomography