Parametric images via dynamic 18F-fluorodeoxyglucose positron emission tomographic data acquisition in predicting midterm outcome of liver metastases secondary to gastrointestinal stromal tumours
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- Apostolopoulos, D.J., Dimitrakopoulou-Strauss, A., Hohenberger, P. et al. Eur J Nucl Med Mol Imaging (2011) 38: 1212. doi:10.1007/s00259-011-1776-2
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18F-Fluorodeoxyglucose positron emission tomography (FDG PET) may underestimate viable tumour tissue in patients with gastrointestinal stromal tumours (GIST) treated with molecular targeted agents. The aim of the present study was to investigate the value of parametric images generated after dynamic data acquisition for the detection of active liver metastases.
The analysis included 65 dynamic FDG PET studies in 34 patients with liver metastases from GIST who were treated with imatinib or sunitinib. Parametric images of intercept and slope were calculated by dedicated software using a voxel-based linear regression of time-activity data. Intercept images represent the tracer’s distribution volume and the slope its overall metabolic turnover. All images were assessed visually and semi-quantitatively. Liver disease status was established 12 months after each PET study. Dichotomous variables of visual interpretation and various quantitative parameters were entered in a statistical model of linear discriminant analysis.
Visual analysis of slope images was more sensitive than the standard 1-h FDG uptake evaluation (70.6 vs 51.0%, p = 0.016) in detecting cases with liver disease progression (n = 51). Specificity did not differ. Combination of all variables in the discriminant analysis model correctly classified 87.7% of cases as progressive or non-progressive disease. Sensitivity was raised to 88.2%.
Parametric images of intercept and slope add a new dimension to the interpretation of FDG PET studies, by isolating visually and quantifying the perfusion and phosphorylation-dependent part of tracer uptake. In treated GIST patients, integration of this information with the 1-h uptake data achieves better characterization of hepatic lesions with respect to disease activity.