Abstract
Radiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of image feature extraction techniques (ETs); these are (i) handcrafted ETs that encompass visual imaging characteristics, curated from knowledge of human experts and, (ii) deep ETs that quantify abstract-level imaging characteristics from large data. Prior studies therefore failed to leverage the complementary information that are accessible from fusing the ETs. In this study, we propose a fused feature signature (FF(Sig)): a selection of image features from handcrafted and deep ETs (e.g., transfer learning and fine-tuning of deep learning models). We evaluated the FF(Sig)'s ability to better represent RRs compared to individual ET approaches with two public datasets: the first dataset was used to build the FF(Sig) using 89 patients with non-small cell lung cancer (NSCLC) comprising of gene expression data and CT images of the thorax and the upper abdomen for each patient; the second NSCLC dataset comprising of 117 patients with CT images and RNA-Seq data and was used as the validation set. Our results show that our FF(Sig) encoded complementary imaging characteristics of tumours and identified more RRs with a broader range of genes that are related to important biological functions such as tumourigenesis. We suggest that the FF(Sig) has the potential to identify important RRs that may assist cancer diagnosis and treatment in the future.