UNLABELLED: (18)F-fluoroazomycinarabinoside ((18)F-FAZA) is a hypoxia-specific PET tracer. In future clinical applications of hypoxia imaging, such as early response monitoring or radiation therapy dose painting, accurate quantification of tracer uptake at the voxel level will be required. The aim of the present study was to assess the validity of parametric methods for the quantification of (18)F-FAZA studies. METHODS: Dynamic 70-min (18)F-FAZA scans were obtained from 9 non-small cell lung cancer patients. Arterial blood samples, collected at 7 time points, were used for preprocessing an image-derived input function derived from volumes of interest (VOIs) defined within the ascending aorta. Time-activity curves derived from various tumor VOIs were fitted using nonlinear regression analysis (NLR) to a reversible 2-tissue-compartment model, providing volumes of distribution (V(T)) as an outcome measure. Next, parametric images were generated by use of both Logan graphic analysis with various linear regression start times and spectral analysis with multiple sets of basis functions. The previously defined tumor VOIs were projected onto these parametric images, and the resulting V(T) were compared with those obtained from NLR. In addition, the results were compared with tumor-to-blood ratios (SUVr), which are more easily obtainable. RESULTS: The highest correlations and correspondence with NLR-derived V(T) were found for Logan graphic analysis with a start time of 30 min after injection (R(2), 0.88; intraclass correlation coefficient [ICC], 0.93) and for spectral analysis-derived V(T) with a set of 30 basis functions with exponents ranging from 0.0175 to 1.9 (R(2), 0.79; ICC, 0.81). SUVr yielded similar correlations but showed significant bias at high V(T) (R(2), 0.85; ICC, 0.80). CONCLUSION: Both Logan graphic analysis and spectral analysis yielded V(T) that showed high correlations with nonlinear regression analysis-derived V(T). SUVr showed bias at high V(T).
UNLABELLED: (18)F-fluoroazomycinarabinoside ((18)F-FAZA) is a hypoxia-specific PET tracer. In future clinical applications of hypoxia imaging, such as early response monitoring or radiation therapy dose painting, accurate quantification of tracer uptake at the voxel level will be required. The aim of the present study was to assess the validity of parametric methods for the quantification of (18)F-FAZA studies. METHODS: Dynamic 70-min (18)F-FAZA scans were obtained from 9 non-small cell lung cancerpatients. Arterial blood samples, collected at 7 time points, were used for preprocessing an image-derived input function derived from volumes of interest (VOIs) defined within the ascending aorta. Time-activity curves derived from various tumor VOIs were fitted using nonlinear regression analysis (NLR) to a reversible 2-tissue-compartment model, providing volumes of distribution (V(T)) as an outcome measure. Next, parametric images were generated by use of both Logan graphic analysis with various linear regression start times and spectral analysis with multiple sets of basis functions. The previously defined tumor VOIs were projected onto these parametric images, and the resulting V(T) were compared with those obtained from NLR. In addition, the results were compared with tumor-to-blood ratios (SUVr), which are more easily obtainable. RESULTS: The highest correlations and correspondence with NLR-derived V(T) were found for Logan graphic analysis with a start time of 30 min after injection (R(2), 0.88; intraclass correlation coefficient [ICC], 0.93) and for spectral analysis-derived V(T) with a set of 30 basis functions with exponents ranging from 0.0175 to 1.9 (R(2), 0.79; ICC, 0.81). SUVr yielded similar correlations but showed significant bias at high V(T) (R(2), 0.85; ICC, 0.80). CONCLUSION: Both Logan graphic analysis and spectral analysis yielded V(T) that showed high correlations with nonlinear regression analysis-derived V(T). SUVr showed bias at high V(T).
Authors: Li Wang; Hui Wang; Kun Shen; Hyejin Park; Tao Zhang; Xuedan Wu; Mei Hu; Hong Yuan; Yue Chen; Zhanhong Wu; Qiu Wang; Zibo Li Journal: J Med Chem Date: 2021-04-26 Impact factor: 7.446