Introduction: Only a subgroup of non-small cell lung cancer (NSCLC) patients benefit from treatment using epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) such as afatinib. Tumour uptake of [18F]afatinib using positron emission tomography (PET) may identify those patients that respond to afatinib therapy. Therefore, the aim of this study was to find the optimal tracer kinetic model for quantification of [18F]afatinib uptake in NSCLC tumours. Methods: [18F]Afatinib PET scans were performed in 10 NSCLC patients. The first patient was scanned for the purpose of dosimetry. Subsequent patients underwent a 20-min dynamic [15O]H2O PET scan (370 MBq) followed by a dynamic [18F]afatinib PET scan (342 ± 24 MBq) of 60 or 90 min. Using the Akaike information criterion (AIC), three pharmacokinetic plasma input models were evaluated with both metabolite-corrected sampler-based input and image-derived (IDIF) input functions in combination with discrete blood samples. Correlation analysis of arterial on-line sampling versus IDIF was performed. In addition, perfusion dependency and simplified measures were assessed. Results: Ten patients were included. The injected activity of [18F]afatinib was 341 ± 37 MBq. Fifteen tumours could be identified in the field of view of the scanner. Based on AIC, tumour kinetics were best described using an irreversible two-tissue compartment model and a metabolite-corrected sampler-based input function (Akaike 50%). Correlation of plasma-based input functions with metabolite-corrected IDIF was very strong (r2 = 0.93). The preferred simplified uptake parameter was the tumour-to-blood ratio over the 60- to 90-min time interval (TBR60–90). Tumour uptake of [18F]afatinib was independent of perfusion. Conclusion: The preferred pharmacokinetic model for quantifying [18F]afatinib uptake in NSCLC tumours was the 2T3K_vb model. TBR60–90 showed excellent correlation with this model and is the best candidate simplified method. Trial registration: https://eudract.ema.europa.eu/ nr 2012-002849-38

Original languageEnglish
Article number97
JournalEJNMMI Research
Issue number1
Publication statusPublished - 17 Aug 2020

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