Radiomic biomarkers of tumor immune biology and immunotherapy response

Jarey H Wang, Kareem A Wahid, Lisanne V van Dijk, Keyvan Farahani, Reid F Thompson, Clifton David Fuller

Research output: Contribution to journalReview articlepeer-review

25 Citations (Scopus)

Abstract

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Original languageEnglish
Pages (from-to)97-115
Number of pages19
JournalClinical and Translational Radiation Oncology
Volume28
DOIs
Publication statusPublished - May-2021
Externally publishedYes

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