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Methods to monitor response to chemotherapy in non-small cell lung cancer with 18F-FDG PET

  • Corneline J. Hoekstra
  • , Otto S. Hoekstra
  • , Sigrid G. Stroobants
  • , Johan Vansteenkiste
  • , Johan Nuyts
  • , Egbert F. Smit
  • , Maarten Boers
  • , Jos W.R. Twisk
  • , Adriaan A. Lammertsma*
  • *Corresponding author for this work
  • Vrije Universiteit Amsterdam

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

PET using 18F-FDG is a promising technique to monitor response in oncology. Unfortunately, a multitude of analytic methods is in use. To date, it is not clear whether simplified methods could replace complex quantitative methods in routine clinical practice. The aim of this study was to select those methods that would qualify for further assessment in a future prospective response-monitoring study by comparing results with patient outcome. Methods: Dynamic 18F-FDG PET scans were obtained on 2 groups of patients. First, 10 patients with advanced non-small cell lung cancer (NSCLC) were scanned on consecutive days before treatment to assess test-retest variability. Second, 30 scans were obtained on 19 patients with locally advanced NSCLC as part of an ongoing response-monitoring study. These scans were analyzed by 2 observers to assess observer variability. In addition, these studies were used to compare various methods with the gold standard, full kinetic analysis (nonlinear regression [NLR]). Results: Using an imagederived input function, NLR showed excellent test-retest and observer agreement confirming that it could be used as a gold standard method. From a total of 34 analytic methods, 10 showed good correlation with NLR. Taking into account the degree of complexity of the methods, 4 remain for further evaluation. Conclusion: The optimal method for analysis of 18F-FDG PET data was determined for several levels of complexity. Four methods need to be evaluated further to determine the optimal trade-off between simplicity and accuracy for routine clinical practice.

Original languageEnglish
Pages (from-to)1304-1309
Number of pages6
JournalJournal of nuclear medicine
Volume43
Issue number10
Publication statusPublished - 1 Oct 2002

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • F-FDG PET
  • Cancer
  • Methodology
  • Response monitoring

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