Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small Cell Lung Cancer

Abstract

Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after surgery. Recently, many machine learning methods have been proposed for recurrence prediction. The methods using gene expression data achieve high accuracy rates but expensive. While, the radiomics features using computer tomography (CT) image is a cost-effective method, but their accuracy is not competitive. In this paper, we propose a genotype-guided radiomics method (GGR) for obtaining high prediction accuracy at a low cost. We used a public radiogenomics dataset of NSCLC, which includes CT images and gene expression data. Our proposed method is two steps method that uses two models. The first model is a gene estimation model, which is used to estimate the gene expression from radiomics features and deep features extracted from CT images. The second model is used to predict the recurrence using the estimated gene. The proposed GGR method is designed based on hybrid features which is the fusion of handcrafted- and deep learning-based features. The experiments demonstrated that the prediction accuracy can be improved significantly from 78.61% (existing radiomics method) and 79.09% (ResNet50) to 83.28% by the proposed GGR.

Keywords

• Non-small cell lung cancer
• prediction of recurrence
• radiogenomics
• genotype-guided radiomics