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Background: A murine breast cancer xenograft model was employed to evaluate inter- and intra-variability of various parameters derived from dynamic positron emission tomography with [18F]-fluorodeoxyglucose as tracer (FDG-PET)., Material and Methods: Seventeen female athymic nude foxn1/nu mice with bilaterally implanted triple-negative basal-like ductal carcinoma (MAS98.12) breast cancer xenografts underwent a dynamic PET scan over an hour after injection of approximately 10 MBq FDG. Inter-animal data were obtained from the entire animal cohort, while intra-animal data were from four mice receiving an additional scan after one or two days. Standardised uptake values (SUVmax, SUVmean and SUVmedian) were estimated for all tumours at different time points. Tumour uptake was analysed with a kinetic two-compartment model for estimation of pharmacokinetic parameters. The coefficient of variation (CV) was calculated for all PET-derived metrics., Results: The CVs for SUVmean and SUVmedian were typically 10-20% for the tumours, depending on the time post-injection and group (intra vs. inter). The CV for SUVmax was mostly higher. The variability in the pharmacokinetic parameters ranged from 23 to almost 150%., Conclusions: SUVmean and SUVmedian show less variability than SUVmax. The pharmacokinetic tumour metrics again display much greater variability than the SUV-based metrics. However, it is generally not known which of these metrics that best represents cancer aggressiveness and their use may still depend on the research questions addressed.

Purpose: The outcome of biologic image-guided radiotherapy depends on the definition of the biologic target. The purpose of the current work was to extract hyperperfused and hypermetabolic regions from dynamic positron emission tomography (D-PET) images, to dose escalate either region and to discuss implications of such image guided strategies., Methods: Eleven patients with soft tissue sarcomas were investigated with D-PET. The images were analyzed using a two-compartment model producing parametric maps of perfusion and metabolic rate. The two image series were segmented and exported to a treatment planning system, and biological target volumes BTVper and BTVmet (perfusion and metabolism, respectively) were generated. Dice's similarity coefficient was used to compare the two biologic targets. Intensity-modulated radiation therapy (IMRT) plans were generated for a dose painting by contours regime, where planning target volume (PTV) was planned to 60 Gy and BTV to 70 Gy. Thus, two separate plans were created for each patient with dose escalation of either BTVper or BTVmet., Results: BTVper was somewhat smaller than BTVmet (209 ± 170 cm(3) against 243 ± 143 cm(3), respectively; population-based mean and s.d.). Dice's coefficient depended on the applied margin, and was 0.72 ± 0.10 for a margin of 10 mm. Boosting BTVper resulted in mean dose of 69 ± 1.0 Gy to this region, while BTVmet received 67 ± 3.2 Gy. Boosting BTVmet gave smaller dose differences between the respective non-boost DVHs (such as D98)., Conclusions: Dose escalation of one of the BTVs results in a partial dose escalation of the other BTV as well. If tumor aggressiveness is equally pronounced in hyperperfused and hypermetabolic regions, this should be taken into account in the treatment planning.

Purpose: To study soft tissue sarcomas using dynamic positron emission tomography (PET) with the glucose analog tracer [(18)F]fluoro-2-deoxy-D-glucose ((18)FDG), to investigate correlations between derived PET image parameters and clinical characteristics, and to discuss implications of dynamic PET acquisition (D-PET)., Material and Methods: D-PET images of 11 patients with soft tissue sarcomas were analyzed voxel-by-voxel using a compartment tracer kinetic model providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Furthermore, standard uptake values (SUVs) in the early (2 min p.i.; SUVE) and late (45 min p.i.; SUVL) phases of the PET acquisition were obtained. The derived transfer rates K1, k2 and k3, along with the metabolic rate of (18)FDG (MRFDG) and the vascular fraction νp, was fused with the computed tomography (CT) images for visual interpretation. Correlations between D-PET imaging parameters and clinical parameters, i.e. tumor size, grade and clinical status, were calculated with a significance level of 0.05., Results: The temporal uptake pattern of (18)FDG in the tumor varied considerably from patient to patient. SUVE peak was higher than SUVL peak for four patients. The images of the rate constants showed a systematic pattern, often with elevated intensity in the tumors compared to surrounding tissue. Significant correlations were found between SUVE/L and some of the rate parameters., Conclusions: Dynamic (18)FDG-PET may provide additional valuable information on soft tissue sarcomas not obtainable from conventional (18)FDG-PET. The prognostic role of dynamic imaging should be investigated.