UMMS Affiliation
Department of Radiology
Publication Date
2022-06-07
Document Type
Article
Disciplines
Oncology | Radiology
Abstract
Treatment response assessment by imaging plays a vital role in evaluating changes in solid tumors during oncology therapeutic clinical trials. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is the reference standard imaging response criteria and provides details regarding image acquisition, image interpretation and categorical response classification. While RECIST 1.1 is applied for the majority of clinical trials in solid tumors, other criteria and modifications have been introduced when RECIST 1.1 outcomes may be incomplete. Available criteria beyond RECIST 1.1 can be explored in an algorithmic fashion dependent on imaging modality, tumor type and method of treatment. Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) is available for use with PET/CT. Modifications to RECIST 1.1 can be tumor specific, including mRECIST for hepatocellular carcinoma and mesothelioma. Choi criteria for gastrointestinal stromal tumors incorporate tumor density with alterations to categorical response thresholds. Prostate Cancer Working Group 3 (PCWG3) imaging criteria combine RECIST 1.1 findings with those of bone scans. In addition, multiple response criteria have been created to address atypical imaging responses in immunotherapy.
Keywords
Cancer imaging, RECIST, RECIST, Response Evaluation Criteria in Solid Tumors, Response assessment, Treatment outcomes
Rights and Permissions
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
DOI of Published Version
10.1016/j.ejro.2022.100426
Source
Ruchalski K, Dewan R, Sai V, McIntosh LJ, Braschi-Amirfarzan M. Imaging response assessment for oncology: An algorithmic approach. Eur J Radiol Open. 2022 Jun 7;9:100426. doi: 10.1016/j.ejro.2022.100426. PMID: 35693043; PMCID: PMC9184854. Link to article on publisher's site
Journal/Book/Conference Title
European journal of radiology open
Related Resources
PubMed ID
35693043
Repository Citation
Ruchalski K, Dewan R, Sai V, McIntosh LJ, Braschi-Amirfarzan M. (2022). Imaging response assessment for oncology: An algorithmic approach. Radiology Publications. https://doi.org/10.1016/j.ejro.2022.100426. Retrieved from https://escholarship.umassmed.edu/radiology_pubs/704
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.