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Radiogenomic map linking image features and gene expression profiles isuseful for noninvasively identifying molecular properties of a particular typeof disease. Conventionally, such map is produced in three separate steps: 1)gene-clustering to "metagenes", 2) image feature extraction, and 3) statisticalcorrelation between metagenes and image features. Each step is independentlyperformed and relies on arbitrary measurements. In this work, we investigatethe potential of an end-to-end method fusing gene data with image features togenerate synthetic image and learn radiogenomic map simultaneously. To achievethis goal, we develop a generative adversarial network (GAN) conditioned onboth background images and gene expression profiles, synthesizing thecorresponding image. Image and gene features are fused at different scales toensure the realism and quality of the synthesized image. We tested our methodon non-small cell lung cancer (NSCLC) dataset. Results demonstrate that theproposed method produces realistic synthetic images, and provides a promisingway to find gene-image relationship in a holistic end-to-end manner.

Correlation via synthesis: end-to-end nodule image generation and radiogenomic map learning based on generative adversarial network