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Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence
Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614294/ https://www.ncbi.nlm.nih.gov/pubmed/36098343 http://dx.doi.org/10.3348/kjr.2022.0225 |
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author | Kim, Nari Lee, Eun Sung Won, Sang Eun Yang, Mihyun Lee, Amy Junghyun Shin, Youngbin Ko, Yousun Pyo, Junhee Park, Hyo Jung Kim, Kyung Won |
author_facet | Kim, Nari Lee, Eun Sung Won, Sang Eun Yang, Mihyun Lee, Amy Junghyun Shin, Youngbin Ko, Yousun Pyo, Junhee Park, Hyo Jung Kim, Kyung Won |
author_sort | Kim, Nari |
collection | PubMed |
description | Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods. |
format | Online Article Text |
id | pubmed-9614294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-96142942022-11-03 Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence Kim, Nari Lee, Eun Sung Won, Sang Eun Yang, Mihyun Lee, Amy Junghyun Shin, Youngbin Ko, Yousun Pyo, Junhee Park, Hyo Jung Kim, Kyung Won Korean J Radiol Oncologic Imaging Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods. The Korean Society of Radiology 2022-11 2022-09-05 /pmc/articles/PMC9614294/ /pubmed/36098343 http://dx.doi.org/10.3348/kjr.2022.0225 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Oncologic Imaging Kim, Nari Lee, Eun Sung Won, Sang Eun Yang, Mihyun Lee, Amy Junghyun Shin, Youngbin Ko, Yousun Pyo, Junhee Park, Hyo Jung Kim, Kyung Won Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title | Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title_full | Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title_fullStr | Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title_full_unstemmed | Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title_short | Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence |
title_sort | evolution of radiological treatment response assessments for cancer immunotherapy: from irecist to radiomics and artificial intelligence |
topic | Oncologic Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614294/ https://www.ncbi.nlm.nih.gov/pubmed/36098343 http://dx.doi.org/10.3348/kjr.2022.0225 |
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