OBJECTIVES: To retrospectively evaluate concordance rates and predictive values in concordant cases among multiparametric MR techniques and FDG-PET to grade cerebral gliomas. METHODS: Multiparametric MR imaging and FDG-PET were performed in 60 consecutive patients with cerebral gliomas (12 low-grade and 48 high-grade gliomas). As the dichotomic variables, conventional MRI, minimum apparent diffusion coefficient in diffusion-weighted imaging, maximum relative cerebral blood volume ratio in perfusion-weighted imaging, choline/creatine ratio and (lipid and lactate)/creatine ratio in MR spectroscopy, and maximum standardised uptake value ratio in FDG-PET in low- and high-grade gliomas were compared. Their concordance rates and positive/negative predictive values (PPV/NPV) in concordant cases were obtained for the various combinations of multiparametric MR techniques and FDG-PET. RESULTS: There were significant differences between low- and high-grade gliomas in all techniques. Combinations of two, three, four, and five out of the five techniques showed concordance rates of 77.0 ± 4.8%, 65.5 ± 4.0%, 58.3 ± 2.6% and 53.3%, PPV in high-grade concordant cases of 97.3 ± 1.7%, 99.1 ± 1.4%, 100.0 ± 0% and 100.0% and NPV in low-grade concordant cases of 70.2 ± 7.5%, 78.0 ± 6.0%, 80.3 ± 3.4% and 80.0%, respectively. CONCLUSION: Multiparametric MR techniques and FDG-PET have a concordant tendency in a two-tiered classification for the grading of cerebral glioma. If at least two examinations concordantly indicated high-grade gliomas, the PPV was about 95%. KEY POINTS: • Modern imaging techniques can help predict the aggressiveness of cerebral gliomas. • Multiparametric MRI and FDG-PET have a concordant tendency to grade cerebral gliomas. • Their high-grade concordant cases revealed at least 95 % positive predictive values. • Their low-grade concordant cases revealed about 70–80% negative predictive values.
OBJECTIVES: To retrospectively evaluate concordance rates and predictive values in concordant cases among multiparametric MR techniques and FDG-PET to grade cerebral gliomas. METHODS: Multiparametric MR imaging and FDG-PET were performed in 60 consecutive patients with cerebral gliomas (12 low-grade and 48 high-grade gliomas). As the dichotomic variables, conventional MRI, minimum apparent diffusion coefficient in diffusion-weighted imaging, maximum relative cerebral blood volume ratio in perfusion-weighted imaging, choline/creatine ratio and (lipid and lactate)/creatine ratio in MR spectroscopy, and maximum standardised uptake value ratio in FDG-PET in low- and high-grade gliomas were compared. Their concordance rates and positive/negative predictive values (PPV/NPV) in concordant cases were obtained for the various combinations of multiparametric MR techniques and FDG-PET. RESULTS: There were significant differences between low- and high-grade gliomas in all techniques. Combinations of two, three, four, and five out of the five techniques showed concordance rates of 77.0 ± 4.8%, 65.5 ± 4.0%, 58.3 ± 2.6% and 53.3%, PPV in high-grade concordant cases of 97.3 ± 1.7%, 99.1 ± 1.4%, 100.0 ± 0% and 100.0% and NPV in low-grade concordant cases of 70.2 ± 7.5%, 78.0 ± 6.0%, 80.3 ± 3.4% and 80.0%, respectively. CONCLUSION: Multiparametric MR techniques and FDG-PET have a concordant tendency in a two-tiered classification for the grading of cerebral glioma. If at least two examinations concordantly indicated high-grade gliomas, the PPV was about 95%. KEY POINTS: • Modern imaging techniques can help predict the aggressiveness of cerebral gliomas. • Multiparametric MRI and FDG-PET have a concordant tendency to grade cerebral gliomas. • Their high-grade concordant cases revealed at least 95 % positive predictive values. • Their low-grade concordant cases revealed about 70–80% negative predictive values.
Authors: R K Gupta; T F Cloughesy; U Sinha; J Garakian; J Lazareff; G Rubino; L Rubino; D P Becker; H V Vinters; J R Alger Journal: J Neurooncol Date: 2000-12 Impact factor: 4.130
Authors: Farzin Imani; Fernando E Boada; Frank S Lieberman; Denise K Davis; Erin L Deeb; James M Mountz Journal: J Neuroimaging Date: 2010-12-14 Impact factor: 2.486
Authors: T Sugahara; Y Korogi; M Kochi; I Ikushima; T Hirai; T Okuda; Y Shigematsu; L Liang; Y Ge; Y Ushio; M Takahashi Journal: AJR Am J Roentgenol Date: 1998-12 Impact factor: 3.959
Authors: Bhaswati Roy; Rakesh K Gupta; Andrew A Maudsley; Rishi Awasthi; Sulaiman Sheriff; Meng Gu; Nuzhat Husain; Sudipta Mohakud; Sanjay Behari; Chandra M Pandey; Ram K S Rathore; Daniel M Spielman; Jeffry R Alger Journal: Neuroradiology Date: 2013-02-02 Impact factor: 2.804
Authors: N Sadeghi; N D'Haene; C Decaestecker; M Levivier; T Metens; C Maris; D Wikler; D Baleriaux; I Salmon; S Goldman Journal: AJNR Am J Neuroradiol Date: 2007-12-13 Impact factor: 3.825
Authors: Valeria Cuccarini; A Erbetta; M Farinotti; L Cuppini; F Ghielmetti; B Pollo; F Di Meco; M Grisoli; G Filippini; G Finocchiaro; M G Bruzzone; M Eoli Journal: J Neurooncol Date: 2016-01 Impact factor: 4.130
Authors: X J Qiao; B M Ellingson; H J Kim; D J J Wang; N Salamon; M Linetsky; A R Sepahdari; B Jiang; J J Tian; S R Esswein; T F Cloughesy; A Lai; L Nghiemphu; W B Pope Journal: AJNR Am J Neuroradiol Date: 2014-12-26 Impact factor: 3.825
Authors: Vincent Dunet; Anastasia Pomoni; Andreas Hottinger; Marie Nicod-Lalonde; John O Prior Journal: Neuro Oncol Date: 2015-08-04 Impact factor: 12.300
Authors: Juan A Guzmán-De-Villoria; José M Mateos-Pérez; Pilar Fernández-García; Enrique Castro; Manuel Desco Journal: Cancer Imaging Date: 2014-12-12 Impact factor: 3.909
Authors: Beatriz Salinas; Christopher P Irwin; Susanne Kossatz; Alexander Bolaender; Gabriela Chiosis; Nagavarakishore Pillarsetty; Wolfgang A Weber; Thomas Reiner Journal: EJNMMI Res Date: 2015-09-04 Impact factor: 3.138