Aarhus University Seal

Jørgen Frøkiær

(18)F-FDG-PET/CT for very early response evaluation predicts CT response in Erlotinib treated NSCLC patients - A comparison of assessment methods

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

The purpose of this study was to determine which method for early response evaluation with (18)F-fluoro-D-glucose positron emission tomography combined with whole body computed tomography ((18)F-FDG-PET/CT) performed most optimal for prediction of response on a later CT-scan in erlotinib treated non-small cell lung cancer (NSCLC) patients. Methods:(18)F-FDG-PET/CT scans were performed prior to and after 7-10 days of erlotinib treatment in 50 NSCLC patients. The scans were evaluated using a qualitative approach and various semi-quantitative methods including percentage change in Standardized Uptake values, lean body mass corrected (SUL) SULpeak, SULmax and Total Lesion Glycolysis (TLG). The PET parameters and their corresponding response categories were compared to the percentage change in the sum of the longest diameter (SLD) in target lesions and the resulting response categories from a CT scan performed after 9-11 weeks of erlotinib treatment using Receiver Operating Characteristics (ROC) analysis, linear regression and quadratic weighted kappa. Results: TLG delineation according to the PET evaluation response criteria in solid tumors (PERCIST) showed the strongest correlation to SLD (R = 0.564, p<0.001), compared to SULmax (R=0.298, P = 0.039) and SULpeak (R=0.402, P = 0.005). For predicting progression on CT, ROC analysis show area under the curves (AUCs) between 0.79 and 0.92 with the highest AUC of 0.92 (95% confidence interval (CI): 0.84-1.00) found for TLG (PERCIST). Furthermore, using a cut-off of 25% change in TLG (PERCIST) for both partial metabolic response (PMR) and progressive metabolic disease (PMD), which is the best predictor of the CT response categories showed a kappa value of 0.53 (95%CI: 0.31-0.75). This method identifies 41% of the later PDs on CT, with no false positives. Visual evaluation correctly identified 50% with a kappa value of 0.47 (95%CI: 0.24-0.70). Conclusion: TLG (PERCIST) was the optimal predictor of response on later CT scans outperforming both SULpeak and SULmax. Using TLG (PERCIST) with a 25% cut-off after 1-2 weeks of treatment allows us to safely identify 41% of the patients who will not benefit from erlotinib and stop the treatment at this time.

Original languageEnglish
JournalJournal of Nuclear Medicine
Volume58
Issue12
Pages (from-to)1931-1937
Number of pages7
ISSN0161-5505
DOIs
Publication statusPublished - 1 Dec 2017

    Research areas

  • Early response evaluation, FDG PET/CT, Lung cancer, Percist 1.0

See relations at Aarhus University Citationformats

ID: 118536105