Multimodal Imaging of Patients With Gliomas Confirms C-MET PET as a Complementary Marker to MRI for Noninvasive Tumor Grading and Intraindividual Follow-Up After Therapy - Kai R. Laukamp MD; Florian Lindemann PhD; Matthias Weckesser MD; Volker Hesselmann MD; Sandra Ligges PhD; Johannes Wölfer MD; Astrid Jeibmann MD; Bastian Zinnhardt PhD; Thomas Viel PhD; Michael Schäfers MD; Werner Paulus MD; Walter Stummer MD; et al. (Molecular Imaging)
The value of combined L-( methyl -[ 11 C]) methionine positron-emitting tomography (MET-PET) and magnetic resonance imaging (MRI) with regard to tumor extent, entity prediction, and therapy effects in clinical routine in patients with suspicion of a brain tumor was investigated. In n = 65 patients with histologically verified brain lesions n = 70 MET-PET and MRI (T1-weighted gadolinium-enhanced [T1w-Gd] and fluid-attenuated inversion recovery or T2-weighted [FLAIR/T2w]) examinations were performed. The computer software “visualization and analysis framework volume rendering engine (Voreen)” was used for analysis of extent and intersection of tumor compartments. Binary logistic regression models were developed to differentiate between World Health Organization (WHO) tumor types/grades. Tumor sizes as defined by thresholding based on tumor-to-background ratios were significantly different as determined by MET-PET (21.6 ± 36.8 cm 3 ), T1w-Gd-MRI (3.9 ± 7.8 cm 3 ), and FLAIR/T2-MRI (64.8 ± 60.4 cm 3 ; P < .001). The MET-PET visualized tumor activity where MRI parameters were negative: PET positive tumor volume without Gd enhancement was 19.8 ± 35.0 cm 3 and without changes in FLAIR/T2 10.3 ± 25.7 cm 3 . FLAIR/T2-MRI visualized greatest tumor extent with differences to MET-PET being greater in posttherapy (64.6 ± 62.7 cm 3 ) than in newly diagnosed patients (20.5 ± 52.6 cm 3 ). The binary logistic regression model differentiated between WHO tumor types (fibrillary astrocytoma II n = 10 from other gliomas n = 16) with an accuracy of 80.8% in patients at primary diagnosis. Combined PET and MRI improve the evaluation of tumor activity, extent, type/grade prediction, and therapy-induced changes in patients with glioma and serve information highly relevant for diagnosis and management.