OBJECTIVES: To investigate the optimal PET protocol and analytical method to characterize the glucose metabolism in nasopharyngeal carcinoma (NPC). METHODS: Newly diagnosed NPC patients were recruited and a dynamic PET-CT scan was performed. The optimized threshold to derive the arterial input function (AIF) was studied. Two-tissue compartmental kinetic modeling using three, four, and five parameters, Patlak graphical analysis, and time sensitivity (S-factor) analysis were performed. The best compartmental model was determined in terms of goodness of fit, and correlated with Ki from Patlak graphical analysis and the S-factor. The methods with R>0.9 and P<0.05 were considered acceptable. The protocols using two static scans with its retention index (RI=(SUV(2)/SUV(1)-1)×100%, where SUV is the standardized uptake value) were also studied and compared with S-factor analysis. RESULTS: The best threshold of 0.6 was determined and used to derive AIF. The kinetic model with five parameters yields the best statistical results, but the model with k4=0 was used as the gold standard. All Ki values and some S-factors from data between various intervals (10-30, 10-45, 15-30, 15-45, 20-30, and 20-45 min) fulfilled the criteria. The RIs calculated from the S-factor were highly correlated to RI derived from simple two-point static scans at 10 and 30 min (R=0.9, P<0.0001). CONCLUSION: The Patlak graphical analyses and even a 20-min-interval S-factor analysis or simple two-point static scans were shown to be sufficient to characterize NPC metabolism, confirming the clinical feasibility of applying a short dynamic with image-derived AIF or simple two-point static PET scans for studying NPC.
OBJECTIVES: To investigate the optimal PET protocol and analytical method to characterize the glucose metabolism in nasopharyngeal carcinoma (NPC). METHODS: Newly diagnosed NPC patients were recruited and a dynamic PET-CT scan was performed. The optimized threshold to derive the arterial input function (AIF) was studied. Two-tissue compartmental kinetic modeling using three, four, and five parameters, Patlak graphical analysis, and time sensitivity (S-factor) analysis were performed. The best compartmental model was determined in terms of goodness of fit, and correlated with Ki from Patlak graphical analysis and the S-factor. The methods with R>0.9 and P<0.05 were considered acceptable. The protocols using two static scans with its retention index (RI=(SUV(2)/SUV(1)-1)×100%, where SUV is the standardized uptake value) were also studied and compared with S-factor analysis. RESULTS: The best threshold of 0.6 was determined and used to derive AIF. The kinetic model with five parameters yields the best statistical results, but the model with k4=0 was used as the gold standard. All Ki values and some S-factors from data between various intervals (10-30, 10-45, 15-30, 15-45, 20-30, and 20-45 min) fulfilled the criteria. The RIs calculated from the S-factor were highly correlated to RI derived from simple two-point static scans at 10 and 30 min (R=0.9, P<0.0001). CONCLUSION: The Patlak graphical analyses and even a 20-min-interval S-factor analysis or simple two-point static scans were shown to be sufficient to characterize NPC metabolism, confirming the clinical feasibility of applying a short dynamic with image-derived AIF or simple two-point static PET scans for studying NPC.
Authors: Mahsa Eskian; Abass Alavi; MirHojjat Khorasanizadeh; Benjamin L Viglianti; Hans Jacobsson; Tara D Barwick; Alipasha Meysamie; Sun K Yi; Shingo Iwano; Bohdan Bybel; Federico Caobelli; Filippo Lococo; Joaquim Gea; Antonio Sancho-Muñoz; Jukka Schildt; Ebru Tatcı; Constantin Lapa; Georgia Keramida; Michael Peters; Raef R Boktor; Joemon John; Alexander G Pitman; Tomasz Mazurek; Nima Rezaei Journal: Eur J Nucl Med Mol Imaging Date: 2018-10-22 Impact factor: 9.236
Authors: Caigang Zhu; Hannah L Martin; Brian T Crouch; Amy F Martinez; Martin Li; Gregory M Palmer; Mark W Dewhirst; Nimmi Ramanujam Journal: Biomed Opt Express Date: 2018-06-27 Impact factor: 3.732
Authors: Bingsheng Huang; Ching-Yee Oliver Wong; Vincent Lai; Dora Lai-Wan Kwong; Pek-Lan Khong Journal: Biomed Res Int Date: 2015-04-30 Impact factor: 3.411