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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
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.
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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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