UNLABELLED: The 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. METHODS: The 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. RESULTS: The 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%). CONCLUSION: In 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.
UNLABELLED: The 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. METHODS: The 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. RESULTS: The 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%). CONCLUSION: In 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.
Authors: Lisa A Hammond; Louis Denis; Umber Salman; Paul Jerabek; Charles R Thomas; John G Kuhn Journal: Invest New Drugs Date: 2003-08 Impact factor: 3.850
Authors: Lihui Wei; Yubin Miao; Fabio Gallazzi; Thomas P Quinn; Michael J Welch; Amy L Vāvere; Jason S Lewis Journal: Nucl Med Biol Date: 2007-09-04 Impact factor: 2.408
Authors: Gang Ren; Zhe Liu; Zheng Miao; Hongguang Liu; Murugesan Subbarayan; Frederick T Chin; Lan Zhang; Sanjiv S Gambhir; Zhen Cheng Journal: J Nucl Med Date: 2009-10-16 Impact factor: 10.057