How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc..
OBJECTIVES: Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection.
MATERIALS AND METHODS: Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth.
RESULTS: Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively.
CONCLUSIONS: Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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Enthalten in: |
Investigative radiology - (2023) vom: 03. Nov. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Tareco Bucho, Teresa M [VerfasserIn] |
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Anmerkungen: |
Date Revised 17.11.2023 published: Print-Electronic Citation Status Publisher |
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doi: |
10.1097/RLI.0000000000001045 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM364136359 |
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500 | |a published: Print-Electronic | ||
500 | |a Citation Status Publisher | ||
520 | |a Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. | ||
520 | |a OBJECTIVES: Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection | ||
520 | |a MATERIALS AND METHODS: Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth | ||
520 | |a RESULTS: Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively | ||
520 | |a CONCLUSIONS: Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable | ||
650 | 4 | |a Journal Article | |
700 | 1 | |a Tissier, Renaud L M |e verfasserin |4 aut | |
700 | 1 | |a Groot Lipman, Kevin B W |e verfasserin |4 aut | |
700 | 1 | |a Bodalal, Zuhir |e verfasserin |4 aut | |
700 | 1 | |a Delli Pizzi, Andrea |e verfasserin |4 aut | |
700 | 1 | |a Nguyen-Kim, Thi Dan Linh |e verfasserin |4 aut | |
700 | 1 | |a Beets-Tan, Regina G H |e verfasserin |4 aut | |
700 | 1 | |a Trebeschi, Stefano |e verfasserin |4 aut | |
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