Artificial Intelligence-based Radiomics in the Era of Immuno-oncology

Cyra Y. Kang, Samantha E. Duarte, Hye Sung Kim, Eugene Kim, Jonghanne Park, Alice Daeun Lee, Yeseul Kim, Leeseul Kim, Sukjoo Cho, Yoojin Oh, Gahyun Gim, Inae Park, Dongyup Lee, Mohamed Abazeed, Yury S. Velichko, Young Kwang Chae*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations


The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.

Original languageEnglish (US)
Pages (from-to)E471-E483
Issue number6
StatePublished - Jun 2022


  • automated intelligence
  • biomarker
  • immuno-oncology
  • immunotherapy
  • machine learning
  • radiomics
  • response prediction
  • tumor heterogeneity

ASJC Scopus subject areas

  • Medicine(all)


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