  Non-small cell lung cancer (NSCLC) is a serious disease and has a high
recurrence rate after the surgery. Recently, many machine learning methods have
been proposed for recurrence prediction. The methods using gene data have high
prediction accuracy but require high cost. Although the radiomics signatures
using only CT image are not expensive, its accuracy is relatively low. In this
paper, we propose a genotype-guided radiomics method (GGR) for obtaining high
prediction accuracy with low cost. We used a public radiogenomics dataset of
NSCLC, which includes CT images and gene data. The proposed method is a
two-step method, which consists of 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 computer tomography (CT) image. The
second model is used to predict the recurrence using the estimated gene
expression data. The proposed GGR method designed based on hybrid features
which is combination of handcrafted-based and deep learning-based. The
experiments demonstrated that the prediction accuracy can be improved
significantly from 78.61% (existing radiomics method) and 79.14% (deep learning
method) to 83.28% by the proposed GGR.
