Literature DB >> 30398433

Validity of RECIST Version 1.1 for Response Assessment in Metastatic Cancer: A Prospective, Multireader Study.

Christiane K Kuhl1, Yunus Alparslan1, Jonas Schmoee1, Bruno Sequeira1, Annika Keulers1, Tim H Brümmendorf1, Sebastian Keil1.   

Abstract

Purpose To determine the relationship between target lesion selection with use of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and classification of therapeutic response in patients with metastatic cancer undergoing systemic cytotoxic and/or targeted therapies. Materials and Methods This prospective multireader study was conducted between July 2015 and July 2017. Three hundred sixteen consecutive participants with metastatic cancer underwent 932 CT examinations to monitor systemic treatment. CT studies were independently read by three radiologists. Readers identified a maximum of five lesions total (and a maximum of two lesions per organ). Dedicated oncology tumor response software was used. The Fleiss κ statistic was used to analyze interreader agreement in the assignment of individual response classes (complete response, partial response, progressive disease, or stable disease) and in the differentiation between progressive and nonprogressive disease. Results Readers selected the same set of target lesions in 128 of the 316 participants (41%) and selected a different set in 188 (59%). When target lesion selection was concordant, agreement was high (assignment of treatment response category: κ = 0.97; 95% confidence interval [CI]: 0.91, 1.0; differentiation between progressive and nonprogressive disease: κ = 0.98; 95% CI: 0.90, 1.0). When target lesion selection was discordant, agreement was significantly reduced (assignment of treatment response category: κ = 0.58; 95% CI: 0.54, 0.62; differentiation between progressive and nonprogressive disease: κ = 0.6; 95% CI: 0.59, 0.70). With concordant target lesion selection, readers agreed regarding diagnosis of progression in 97.7% of participants (95% CI: 95.4%, 100.0%); with discordant target lesion selection, readers agreed in only 55.3% (95% CI: 47.9%, 62.6%) (P < .01). Conclusion In patients with metastatic cancer undergoing systemic treatment, different cancer sites may appear similarly suitable and thus likely to be selected as target lesions but may yield inconsistent or even conflicting results with Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. This indicates that the current, limited set of target lesions in RECIST 1.1 may not reflect overall tumor load or response to therapy. © RSNA, 2018 See also the editorial by Sosna in this issue.

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Year:  2018        PMID: 30398433     DOI: 10.1148/radiol.2018180648

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  16 in total

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Authors:  Roland Hustinx
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-07       Impact factor: 9.236

2.  A Primer on RECIST 1.1 for Oncologic Imaging in Clinical Drug Trials.

Authors:  Kathleen Ruchalski; Marta Braschi-Amirfarzan; Michael Douek; Victor Sai; Antonio Gutierrez; Rohit Dewan; Jonathan Goldin
Journal:  Radiol Imaging Cancer       Date:  2021-05

3.  Variability of quantitative measurements of metastatic liver lesions: a multi-radiation-dose-level and multi-reader comparison.

Authors:  Yuqin Ding; Daniele Marin; Federica Vernuccio; Fernando Gonzalez; Hannah V Williamson; Hans-Christoph Becker; Bhavik N Patel; Justin Solomon; Juan Carlos Ramirez-Giraldo; Ehsan Samei; Rendon C Nelson; Mathias Meyer
Journal:  Abdom Radiol (NY)       Date:  2020-06-10

4.  Assessment of the response of hepatocellular carcinoma to interventional radiology treatments.

Authors:  Francesca Patella; Filippo Pesapane; Enrico Fumarola; Stefania Zannoni; Pietro Brambillasca; Ilaria Emili; Guido Costa; Victoria Anderson; Elliot B Levy; Gianpaolo Carrafiello; Bradford J Wood
Journal:  Future Oncol       Date:  2019-05-02       Impact factor: 3.404

5.  Intra- and inter-reader agreement of iRECIST and RECIST 1.1 criteria for the assessment of tumor response in patients receiving checkpoint inhibitor immunotherapy for lung cancer.

Authors:  Sandra Huicochea Castellanos; Andrew Pagano; Andrew J Plodkowski; Jeffrey Girshman; Matthew D Hellmann; Hira Rizvi; Jessica Flynn; Junting Zheng; Marinela Capanu; Darragh F Halpenny; Michelle S Ginsberg
Journal:  Lung Cancer       Date:  2021-09-04       Impact factor: 5.705

6.  Standardized Reporting of Oncologic Response: Making Every Report Count.

Authors:  Richard K G Do; Robert A Lefkowitz; Vaios Hatzoglou; Weining Ma; Krishna Juluru; Marius Mayerhoefer
Journal:  Radiol Imaging Cancer       Date:  2022-06

Review 7.  Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

Authors:  Kaustav Bera; Nathaniel Braman; Amit Gupta; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Nat Rev Clin Oncol       Date:  2021-10-18       Impact factor: 65.011

Review 8.  What scans we will read: imaging instrumentation trends in clinical oncology.

Authors:  Thomas Beyer; Luc Bidaut; John Dickson; Marc Kachelriess; Fabian Kiessling; Rainer Leitgeb; Jingfei Ma; Lalith Kumar Shiyam Sundar; Benjamin Theek; Osama Mawlawi
Journal:  Cancer Imaging       Date:  2020-06-09       Impact factor: 3.909

Review 9.  Consensus report from the 9th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging.

Authors:  Dow-Mu Koh; Ahmed Ba-Ssalamah; Giuseppe Brancatelli; Ghaneh Fananapazir; M Isabel Fiel; Satoshi Goshima; Sheng-Hong Ju; Nikolaos Kartalis; Masatoshi Kudo; Jeong Min Lee; Takamichi Murakami; Max Seidensticker; Claude B Sirlin; Cher Heng Tan; Jin Wang; Jeong Hee Yoon; Mengsu Zeng; Jian Zhou; Bachir Taouli
Journal:  Eur Radiol       Date:  2021-02-01       Impact factor: 5.315

10.  QIN Benchmarks for Clinical Translation of Quantitative Imaging Tools.

Authors:  Keyvan Farahani; Darrell Tata; Robert J Nordstrom
Journal:  Tomography       Date:  2019-03
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