PURPOSE: (1) To investigate the diagnostic value of some O-(2-[F]fluoroethyl)-L-tyrosine (F-18 FET) indices derived from the dynamic acquisition to differentiate low-grade gliomas from high-grade; (2) to analyze the course of tumor time-activity curves (TACs); and (3) to calculate the individual probability of a high-grade glioma using the logistic regression. METHODS: Seventeen low-grade (WHO I-II) and 15 high-grade (WHO III-IV) gliomas were studied with dynamic F-18 FET PET. Regions of interests were drawn over the tumor and contralateral brain, and TACs were analyzed. We considered early standardized uptake value (SUV), middle SUV, late SUV, early-to-middle SUV tumor ratio, early-to-late SUV tumor ratio; time to peak (Tpeak), in minutes, from the beginning of the dynamic acquisition up to the maximum SUV of the tumor; and SoD (sum of the frame-to-frame differences). To assess the individual probability of high-grade, logistic regression was also used. RESULTS: High-grade gliomas showed significantly (P < 0.0001) higher values when compared with low-grade gliomas in early SUV, early-to-middle ratio, early-to-late ratio, Tpeak, and SoD. For the grading of gliomas, the best indices were early-to-middle ratio and Tpeak providing a diagnostic accuracy of 94%. TACs analysis provided an 87% diagnostic accuracy. For individual high-grade diagnosis, the logistic regression provided 93% sensitivity, 100% specificity, and 97% accuracy. CONCLUSION: Early-to-middle SUV tumor ratio and Tpeak were the best indices for assessing the grading of gliomas. Since early-to-middle ratio derives from the first 35 minutes of the dynamic acquisition, the PET study could last half an hour instead of 1 hour. By logistic regression, it is possible to assess the individual probability of high-grade, useful for prognosis and treatment.
PURPOSE: (1) To investigate the diagnostic value of some O-(2-[F]fluoroethyl)-L-tyrosine (F-18 FET) indices derived from the dynamic acquisition to differentiate low-grade gliomas from high-grade; (2) to analyze the course of tumor time-activity curves (TACs); and (3) to calculate the individual probability of a high-grade glioma using the logistic regression. METHODS: Seventeen low-grade (WHO I-II) and 15 high-grade (WHO III-IV) gliomas were studied with dynamic F-18 FET PET. Regions of interests were drawn over the tumor and contralateral brain, and TACs were analyzed. We considered early standardized uptake value (SUV), middle SUV, late SUV, early-to-middle SUV tumor ratio, early-to-late SUV tumor ratio; time to peak (Tpeak), in minutes, from the beginning of the dynamic acquisition up to the maximum SUV of the tumor; and SoD (sum of the frame-to-frame differences). To assess the individual probability of high-grade, logistic regression was also used. RESULTS: High-grade gliomas showed significantly (P < 0.0001) higher values when compared with low-grade gliomas in early SUV, early-to-middle ratio, early-to-late ratio, Tpeak, and SoD. For the grading of gliomas, the best indices were early-to-middle ratio and Tpeak providing a diagnostic accuracy of 94%. TACs analysis provided an 87% diagnostic accuracy. For individual high-grade diagnosis, the logistic regression provided 93% sensitivity, 100% specificity, and 97% accuracy. CONCLUSION: Early-to-middle SUV tumor ratio and Tpeak were the best indices for assessing the grading of gliomas. Since early-to-middle ratio derives from the first 35 minutes of the dynamic acquisition, the PET study could last half an hour instead of 1 hour. By logistic regression, it is possible to assess the individual probability of high-grade, useful for prognosis and treatment.
Authors: Nathalie L Albert; Isabel Winkelmann; Bogdana Suchorska; Vera Wenter; Christine Schmid-Tannwald; Erik Mille; Andrei Todica; Matthias Brendel; Jörg-Christian Tonn; Peter Bartenstein; Christian la Fougère Journal: Eur J Nucl Med Mol Imaging Date: 2015-12-15 Impact factor: 9.236
Authors: Manuel Röhrich; Kristin Huang; Daniel Schrimpf; Nathalie L Albert; Thomas Hielscher; Andreas von Deimling; Ulrich Schüller; Antonia Dimitrakopoulou-Strauss; Uwe Haberkorn Journal: Eur J Nucl Med Mol Imaging Date: 2018-05-07 Impact factor: 9.236
Authors: Talia C Oughourlian; Jingwen Yao; Jacob Schlossman; Catalina Raymond; Matthew Ji; Hiroyuki Tatekawa; Noriko Salamon; Whitney B Pope; Johannes Czernin; Phioanh L Nghiemphu; Albert Lai; Timothy F Cloughesy; Benjamin M Ellingson Journal: J Neurooncol Date: 2020-01-24 Impact factor: 4.130
Authors: Garry Ceccon; Philipp Lohmann; Gabriele Stoffels; Natalie Judov; Christian P Filss; Marion Rapp; Elena Bauer; Christina Hamisch; Maximilian I Ruge; Martin Kocher; Klaus Kuchelmeister; Bernd Sellhaus; Michael Sabel; Gereon R Fink; Nadim J Shah; Karl-Josef Langen; Norbert Galldiks Journal: Neuro Oncol Date: 2017-02-01 Impact factor: 12.300