TY - JOUR
T1 - Radiomic biomarkers of tumor immune biology and immunotherapy response
AU - Wang, Jarey H.
AU - Wahid, Kareem A.
AU - van Dijk, Lisanne V.
AU - Farahani, Keyvan
AU - Thompson, Reid F.
AU - Fuller, Clifton David
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - Biomarkers
KW - Imaging genomics
KW - Immunotherapy
KW - Precision medicine
KW - Radiogenomics
KW - Radiomics
KW - Tumor immunology
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U2 - 10.1016/j.ctro.2021.03.006
DO - 10.1016/j.ctro.2021.03.006
M3 - Review article
C2 - 33937530
AN - SCOPUS:85104088230
SN - 2405-6308
VL - 28
SP - 97
EP - 115
JO - Clinical and Translational Radiation Oncology
JF - Clinical and Translational Radiation Oncology
ER -