UNLABELLEDWe evaluated quantitative measurement series (MS) with 18F-FDG and PET and compared different quantification methods for prediction of individual survival in patients with metastatic colorectal cancer receiving chemotherapy with 5-fluorouracil, folinic acid, and oxaliplatin (FOLFOX).METHODSThe study comprised 25 patients. All patients were examined before the onset of FOLFOX therapy and after completion of the first and fourth cycles. SUV, fractal dimension (FD), a 2-compartment model with computation of k1, k2, k3, and k4, and vascular fraction (VB) were used for data evaluation. Survival data served as a reference for the PET data. Discriminant analysis (DA), regression, and best-subset analysis were applied to the data.RESULTSTwenty of 25 patients died up to 801 d after the first PET study. A cutoff of 1 y (364 d) was used to classify the patients into 2 a priori groups, namely the short- and long-term survival groups. DA was used to predict the 2 categories using SUV and kinetic parameters of 18F-FDG metabolism as predictor variables. SUV provided a correct classification rate (CCR) ranging from 62% to 69%. SUV of the third MS resulted in a CCR of 69% as a single parameter. The best results were yielded by the use of kinetic parameters (k1, k3, VB, and FD) as predictor variables. CCR was 78% using kinetic 18F-FDG parameters of the first and third MS, in comparison with 69% for the corresponding SUVs. A multiple linear regression model was applied to the data to assess the relationship between individual survival and the PET data. The best-subset method revealed a correlation coefficient of 0.850 for the kinetic parameters of the first (k3, k4, VB, and FD) and third (k1, k2, k4, and VB) MS.CONCLUSIONThe combination of kinetic parameters of the first and the third MS is acceptable for classification into a short or long survival class. Furthermore, even an individual prognosis of survival can be achieved using kinetic 18F-FDG parameters of the first and third MS.

UNLABELLED:The role of quantitative (18)F-FDG PET studies for the differentiation of benign and malignant bone lesions is still an open question.METHODS:Our evaluation included 83 patients with 37 histologically proven malignancies and 46 benign lesions. Thirty-five of the 46 benign lesions were histologically confirmed. The (18)F-FDG studies were accomplished as a dynamic series for 60 min. Evaluation of the (18)F-FDG kinetics was performed using the following parameters: standardized uptake value (SUV), global influx (Ki), computation of the transport constants K1-k4 with consideration of the distribution volume (VB) according to a 2-tissue-compartment model, fractal dimension based on the box-counting procedure (parameter for the inhomogeneity of the tumors).RESULTS:The mean SUV, the vascular fraction VB, K1, and k3 were higher in malignant tumors compared with benign lesions (t test; P < 0.05). Although the (18)F-FDG SUV was helpful to differentiate benign and malignant tumors, there was some overlap, which limited the diagnostic accuracy. On the basis of the discriminant analysis, the SUV alone showed a sensitivity of only 54.05%, a specificity of 91.30%, and a diagnostic accuracy of 74.70%. The fractal dimension was superior and showed a sensitivity of 71.88%, a specificity of 81.58%, and an accuracy of 77.14%. The combination of SUV, fractal dimension, VB, K1-k4, and Ki revealed the best results with a sensitivity of 75.86%, a specificity of 97.22%, and an accuracy of 87.69%. Bayesian analysis showed true-positive results at the level of 0.8 for a low prevalence of disease (0.235) if the full kinetic data were used in the evaluation.CONCLUSION:(18)F-FDG PET has a high specificity for the exclusion of a malignant bone tumor. Evaluation of the full (18)F-FDG kinetics and the application of discriminant analysis are required and can be used prospectively to classify a bone lesion as malignant or benign.

The purpose of this study was to evaluate F-18-FDG PET studies of primary and recurrent sarcomas for diagnosis and correlation with grading. Methods: The evaluation included 56 patients, 43 with histologically proven malignancies and 13 with benign lesions. Seventeen patients were referred with suspicion on a primary tumor, and the remaining 39 were referred with suspicion on a recurrent tumor. The FDG studies were accomplished as a dynamic series for 60 min. The evaluation of the FDG kinetics was performed using the following parameters: standardized uptake value (SUV), global influx, computation of the transport constants K1-k4 with consideration of the distribution volume (VB) according to a two-tissue-compartment model, and fractal dimension based on the box-counting procedure (parameter for the inhomogeneity of the tumors). Results: Visual evaluation revealed a sensitivity of 76.2%, a specificity of 42.9%, and an accuracy of 67.9%. The vascular fraction VB and the SUV were higher in malignant tumors compared with benign lesions (t test, P < 0.05). Although the FDG SUV helped to distinguish benign and malignant tumors, there was some overlap, which limited the diagnostic accuracy. The SUV and fractal dimension accounted for significant differences in six of the nine diagnostic pairs. Whereas grade (G) II and G III tumors were differentiated from lipomas on the basis of the fractal dimension and some other kinetic parameters, no differences were found between G I tumors and lipomas. On the basis of the discriminant analysis, the differentiation of soft-tissue tumors was best for the use of six parameters of the FDG kinetics (SUV. VB, K1, k3, influx, and fractal dimension). Eighty-four percent of G III tumors, 37.5% of G II tumors, 80% of G I tumors, 50% of lipomas, and 14.3% of scars could be classified correctly, whereas inflammatory lesions were misclassified. Conclusion: FDG PET should be used preferentially for monitoring patients with G III sarcomas, Visual analysis provides a low specificity. In contrast, the evaluation of the full FDG kinetics provides superior information, particularly for the discrimination of G I and G III tumors (positive predictive value, >80%).

UNLABELLEDThe purpose of the study was to evaluate the 6-[18F]fluoro-L-dopa (FDOPA) kinetics with PET in patients with treated melanoma metastases and to compare it with the standard tracer 18F-FDG as well as with the perfusion tracer (15)O-water in selected cases.METHODSThe study included 11 patients (22 lesions) with pretreated metastatic melanomas. Dynamic studies with FDG and in selected cases with (15)O-water (eight patients) preceded the FDOPA study. A one-tissue-compartment model was used for the evaluation of the FDOPA and (15)O-water studies, and a two-tissue-compartment model and Patlak analysis were used for the FDG data. A noncompartment model based on chaos theory was used for calculating fractal dimension, which is a parameter for heterogeneity.RESULTSThe FDG studies showed a 1.5-fold increased uptake in comparison with surrounding tissue in 19 of 22 metastatic lesions (sensitivity of 86.4%). False-negative FDG results were obtained in 2 patients (three lesions). FDOPA uptake was enhanced in 14 of 22 metastatic lesions (sensitivity of 64%). FDG uptake was 1.5-fold higher than FDOPA uptake in 18 of 22 metastases from melanoma, whereas FDOPA uptake was 1.5-fold higher than FDG uptake in 2 patients with liver metastases. The data did not show a statistically significant correlation between the transport constant (K1) for FDOPA and that for FDG or between the standardized uptake value for FDOPA and FDG in metastases. No statistically significant correlation was found between K1 for FDOPA and that for (15)O-water. The data show that FDOPA uptake is not perfusion dependent and provides different information from FDG. The fractal dimension was similar for all tracers within the tumor region. Detectability of metastases was enhanced when both tracers were used (sensitivity of 95%).CONCLUSIONIn patients with negative FDG findings, FDOPA can help to identify viable melanoma metastases and thus may help to select patients who would benefit from further treatment.