Extendable and explainable deep learning for pan-cancer radiogenomics research

Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging bio-markers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its appli-cation to pan-cancer radiogenomics, which are extendibility and explainability.

Automated summary

Radiogenomics is a field that combines medical images and genomic analysis to address important clinical questions. This review provides an overview of the current state, limitations, and potential future directions of deep learning in pan-cancer radiogenomic research, while also briefly discussing traditional machine learning methods and research resources. The extendibility and explainability of deep learning in pan-cancer radiogenomics are emphasized.