Background: Precision medicine, a popular treatment strategy, has become increasingly important to the development of targeted therapy. To correlate medical imaging with prognostic and genomic data, researches in radiomics and radiogenomics have provide many pre-de_ned image features to describe image information quantitatively or qualitatively. However, in previous researches, there are only statistical results which proves high correlation among multi-source medical data, but those can't give intuitive and visual result.
Results: In this paper, a deep learning based radio-genomics framework is provided to construct the linkage from lung tumor images to genomics data and implement generation process in turn, which form a bi-direction framework to map multi-source medical data. The imaging features are extracted from auto-encoder under the condition of genomics data. It can obtain much more relevant features than traditional radio-genomics methods. Finally, we use generative adversarial network to transform genomics data onto tumor images, which gives a cogent result to explain the linkage between them.
Conclusions: Our proposed framework provides a deep learning method to do radio-genomics researches more functionally and intuitively.