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Role of radiomics in predicting immunotherapy response
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional inf...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323544/ https://www.ncbi.nlm.nih.gov/pubmed/35581928 http://dx.doi.org/10.1111/1754-9485.13426 |
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author | Kothari, Gargi |
author_facet | Kothari, Gargi |
author_sort | Kothari, Gargi |
collection | PubMed |
description | Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue‐based biomarkers. Quantitative image analysis or radiomics, which involves the high‐throughput extraction of imaging features, has the potential to non‐invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune‐related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high‐quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well‐defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune‐related adverse effects and less well‐studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models. |
format | Online Article Text |
id | pubmed-9323544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93235442022-07-30 Role of radiomics in predicting immunotherapy response Kothari, Gargi J Med Imaging Radiat Oncol MEDICAL IMAGING—RADIATION ONCOLOGY Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue‐based biomarkers. Quantitative image analysis or radiomics, which involves the high‐throughput extraction of imaging features, has the potential to non‐invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune‐related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high‐quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well‐defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune‐related adverse effects and less well‐studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models. John Wiley and Sons Inc. 2022-05-17 2022-06 /pmc/articles/PMC9323544/ /pubmed/35581928 http://dx.doi.org/10.1111/1754-9485.13426 Text en © 2022 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | MEDICAL IMAGING—RADIATION ONCOLOGY Kothari, Gargi Role of radiomics in predicting immunotherapy response |
title | Role of radiomics in predicting immunotherapy response |
title_full | Role of radiomics in predicting immunotherapy response |
title_fullStr | Role of radiomics in predicting immunotherapy response |
title_full_unstemmed | Role of radiomics in predicting immunotherapy response |
title_short | Role of radiomics in predicting immunotherapy response |
title_sort | role of radiomics in predicting immunotherapy response |
topic | MEDICAL IMAGING—RADIATION ONCOLOGY |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9323544/ https://www.ncbi.nlm.nih.gov/pubmed/35581928 http://dx.doi.org/10.1111/1754-9485.13426 |
work_keys_str_mv | AT kotharigargi roleofradiomicsinpredictingimmunotherapyresponse |