Extendable and explainable deep learning for pan-cancer radiogenomics research

Curr Opin Chem Biol. 2022 Feb;66:102111. doi: 10.1016/j.cbpa.2021.102111. Epub 2022 Jan 6.

Abstract

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 biomarkers 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 application to pan-cancer radiogenomics, which are extendibility and explainability.

Keywords: Explainable deep learning; Extendable deep learning; Pan-cancer; Radiogenomics.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Deep Learning*
  • Diagnostic Imaging
  • Genomics
  • Humans
  • Machine Learning
  • Neoplasms* / diagnostic imaging
  • Neoplasms* / genetics