Anum Aslam, Victoria Chernyak, Frank H Miller, Mustafa Bashir, Richard Do, Claude Sirlin, Robert J Lewandowski, Charles Y Kim, Ania Zofia Kielar, Avinash R Kambadakone, Hooman Yarmohammadi, Edward Kim, Dawn Owen, Resmi A Charalel, Anuradha Shenoy-Bhangle, Lauren M Burke, Mishal Mendiratta-Lala
{"title":"CT/MRI LI-RADS 2024 更新：治疗反应评估。","authors":"Anum Aslam, Victoria Chernyak, Frank H Miller, Mustafa Bashir, Richard Do, Claude Sirlin, Robert J Lewandowski, Charles Y Kim, Ania Zofia Kielar, Avinash R Kambadakone, Hooman Yarmohammadi, Edward Kim, Dawn Owen, Resmi A Charalel, Anuradha Shenoy-Bhangle, Lauren M Burke, Mishal Mendiratta-Lala","doi":"10.1148/radiol.232408","DOIUrl":null,"url":null,"abstract":"<p><p>With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.</p>","PeriodicalId":20896,"journal":{"name":"Radiology","volume":"313 2","pages":"e232408"},"PeriodicalIF":12.1000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605109/pdf/","citationCount":"0","resultStr":"{\"title\":\"CT/MRI LI-RADS 2024 Update: Treatment Response Assessment.\",\"authors\":\"Anum Aslam, Victoria Chernyak, Frank H Miller, Mustafa Bashir, Richard Do, Claude Sirlin, Robert J Lewandowski, Charles Y Kim, Ania Zofia Kielar, Avinash R Kambadakone, Hooman Yarmohammadi, Edward Kim, Dawn Owen, Resmi A Charalel, Anuradha Shenoy-Bhangle, Lauren M Burke, Mishal Mendiratta-Lala\",\"doi\":\"10.1148/radiol.232408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.</p>\",\"PeriodicalId\":20896,\"journal\":{\"name\":\"Radiology\",\"volume\":\"313 2\",\"pages\":\"e232408\"},\"PeriodicalIF\":12.1000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11605109/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1148/radiol.232408\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1148/radiol.232408","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.
期刊介绍：
Published regularly since 1923 by the Radiological Society of North America (RSNA), Radiology has long been recognized as the authoritative reference for the most current, clinically relevant and highest quality research in the field of radiology. Each month the journal publishes approximately 240 pages of peer-reviewed original research, authoritative reviews, well-balanced commentary on significant articles, and expert opinion on new techniques and technologies.
Radiology publishes cutting edge and impactful imaging research articles in radiology and medical imaging in order to help improve human health.
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