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Unlabelled: As the preparation phase of a multicenter clinical trial using (18)F-fluoro-2-deoxy-d-glucose ((18)F-FDG), (18)F-fluoromisonidazole ((18)F-FMISO), and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) in non-small cell lung cancer (NSCLC) patients, we investigated whether 18 nuclear medicine centers would score tracer uptake intensity similarly and define hypoxic and proliferative volumes for 1 patient and we compared different segmentation methods., Methods: Ten (18)F-FDG, ten (18)F-FMISO, and ten (18)F-FLT PET/CT examinations were performed before and during curative-intent radiotherapy in 5 patients with NSCLC. The gold standards for uptake intensity and volume delineation were defined by experts. The between-center agreement (18 nuclear medicine departments connected with a dedicated network, SFMN-net [French Society of Nuclear Medicine]) in the scoring of uptake intensity (5-level scale, then divided into 2 levels: 0, normal; 1, abnormal) was quantified by κ-coefficients (κ). The volumes defined by different physicians were compared by overlap and κ. The uptake areas were delineated with 22 different methods of segmentation, based on fixed or adaptive thresholds of standardized uptake value (SUV)., Results: For uptake intensity, the κ values between centers were, respectively, 0.59 for (18)F-FDG, 0.43 for (18)F-FMISO, and 0.44 for (18)F-FLT using the 5-level scale; the values were 0.81 for (18)F-FDG and 0.77 for both (18)F-FMISO and (18)F-FLT using the 2-level scale. The mean overlap and mean κ between observers were 0.13 and 0.19, respectively, for (18)F-FMISO and 0.2 and 0.3, respectively, for (18)F-FLT. The segmentation methods yielded significantly different volumes for (18)F-FMISO and (18)F-FLT (P < 0.001). In comparison with physicians, the best method found was 1.5 × maximum SUV (SUVmax) of the aorta for (18)F-FMISO and 1.3 × SUVmax of the muscle for (18)F-FLT. The methods using the SUV of 1.4 and the method using 1.5 × the SUVmax of the aorta could be used for (18)F-FMISO and (18)F-FLT. Moreover, for (18)F-FLT, 2 other methods (adaptive threshold based on 1.5 or 1.6 × muscle SUVmax) could be used., Conclusion: The reproducibility of the visual analyses of (18)F-FMISO and (18)F-FLT PET/CT images was demonstrated using a 2-level scale across 18 centers, but the interobserver agreement was low for the (18)F-FMISO and (18)F-FLT volume measurements. Our data support the use of a fixed threshold (1.4) or an adaptive threshold using the aorta background to delineate the volume of increased (18)F-FMISO or (18)F-FLT uptake. With respect to the low tumor-on-background ratio of these tracers, we suggest the use of a fixed threshold (1.4).

Objective: The aim of the study was to evaluate the robustness of the calibration procedure against the counting statistics and lesion volumes when using an adaptive thresholding method for the delineation of 2-[18F]fluoro-2-deoxyglucose (18F-FDG)-PET-positive tissue., Materials and Methods: Three data sets obtained from physical and simulated images of a phantom containing hot spheres of known volume and contrast were used to study the robustness of the calibration procedure against the counting statistics and range of volumes and contrasts for a given PET model. The mathematical expression of the adaptive thresholding method used corresponds to a linear relationship between the optimal threshold value and the inverse of the local contrast. Robustness was evaluated by testing whether the slopes and intercepts of the linear expression found under two experimental conditions were significantly different (P<0.05)., Results: It was found that the calibration step was not sensitive to the PET device for the studied PET model, nor to the counting statistics for a signal-to-noise ratio higher than 5.7. No statistical difference was found in the calibration step when using a wide range of volumes (0.2-200 ml) and contrasts (2.0-20.6) or more restricted ones (0.43-97.3 ml and 2.0-7.7, respectively). Therefore, a calibration procedure using limited experimental conditions can be applied to a wider range of volumes and contrasts., Conclusion: These results show that the manufacturer could propose simulated or experimental raw data corresponding to a given PET model with high counting statistics, allowing each clinical center to reconstruct calibration images according to the algorithm parameters used in the clinic.

Objectives: The objectives were (i) to confirm that diagnostic FDG-PET images could be obtained during thoracic radiotherapy, (ii) to verify that significant changes in FDG uptake or volume could be measured early enough to adapt the radiotherapy plan and (iii) to determine an optimal time window during the radiotherapy course to acquire a single FDG-PET examination that would be representative of tumour response., Methods: Ten non-small cell lung carcinoma (NSCLC) patients with significant PET/CT-FDG tumour radioactivity uptake (versus the background level), candidates for curative radiotherapy (RT, n=4; 60-70 Gy, 2 Gray per fraction, 5 fractions per week) or RT plus chemotherapy (CT-RT, n=6), were prospectively evaluated. Using a Siemens Biograph, 5 or 6 PET/CT scans (PET(n), n=0-5) were performed for each patient. Each acquisition included a 15-min thoracic PET with respiratory gating (RG) 60±5 min post-injection of the FDG (3.5 MBq/kg), followed by a standard, 5-min non-gated (STD) thoracic PET. PET(0) was performed before the first RT fraction. During RT, PET(1-5) were performed every 7 fractions, i.e., at 14 Gy total dose increment. FDG uptake was measured as the variation of SUV(max,PETn) versus SUV(max,PET0). Each lesions' volume was measured by (i) visual delineation by an experienced nuclear physician, (ii) 40% SUV(max) fixed threshold and (iii) a semi-automatic adaptive threshold method., Results: A total of 53 FDG-PET scans were acquired. Seventeen lesions (6 tumours and 11 nodes) were visible on PET(0) in the 10 patients. The lesions were located either in or near the mediastinum or in the apex, without significant respiratory displacements at visual inspection of the gated images. Healthy lung did not cause motion artefacts in the PET images. As measured on 89 lesions, both the absolute and relative SUV(max) values decreased as the RT dose increased. A 50% SUV(max) decrease was obtained around a total dose of 45 Gy. Out of the 89 lesions, 75 remained visually identifiable during the entire course of treatment. The 40% fixed threshold and adaptive threshold methods failed to delineate otherwise visible lesions in 16/33 (48%) and 3/33 (9%) lesions, respectively. The failure rate increased with increasing RT doses. Restricting the analysis to the manually-defined volumes in 89 visible lesions, the relative volumes decreased with increased dose., Conclusions: FDG-PET images can be analysed during thoracic RT, given either alone or with chemotherapy, without disturbing radiation-induced artefacts. An average 50% decrease in SUV(max) was observed around 40-45 Gy (i.e., during week 5 of RT). The three delineation methods yielded consistent volume measurements before RT and during the first week of RT, while manual delineation appeared to be more reliable later on during RT., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Unlabelled: In (18)F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from (18)F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations., Methods: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient (18)F-FDG PET scan. Four threshold-based methods (including one, T(bgd), accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV(max)). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized., Results: In the phantom and simulated data, the T(bgd) and Fit methods yielded the most accurate volume estimates, with mean errors of 2% +/- 11% and -8% +/- 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between -31% and -46%, much larger than the bias observed with SUV(max) (-11% +/- 23%) or with the PVE-corrected SUV based on T(bgd) and Fit (-2% +/- 10% and 3% +/- 24%)., Conclusion: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T(bgd) and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T(bgd) and Fit were more accurate and reproducible than SUV(max).

An iterative generic algorithm has been developed to compare three thresholding models used to delineate gross tumour volume on (18)F-FDG PET images. 3D volume was extracted and characteristic parameters were measured. Three fitting models using different parameters were studied: model 1 (volume, contrast), model 2 (contrast) and model 3 (SUV). The calibration was performed using a cylindrical phantom filled with hot spheres. To validate the models, two other phantoms were used. The calibration procedure showed a better fitting model for model 1 (R(2) from 0.94 to 1.00) than for model 3 (0.95) and model 2 (0.69). The validation study shows that model 3 yielded large volume measurement errors. Models 1 and 2 gave close results with no significant differences. Model 2 was preferred because it presents less error dispersion and needs fewer characteristic parameters, making it easier to implement. Our results show the importance of developing a generic algorithm to compare the performances of fitting models objectively and to validate results on other phantoms than the ones used during the calibration process to avoid methodological biases.