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Purpose: The aim of this study was the development and external validation of a logistic regression model to differentiate gliosarcoma (GSC) and glioblastoma multiforme (GBM) on standard MR imaging., Methods: A univariate and multivariate analysis was carried out of a logistic regression model to discriminate patients histologically diagnosed with primary GSC and an age and sex-matched group of patients with primary GBM on presurgical MRI with external validation., Results: In total, 56 patients with GSC and 56 patients with GBM were included. Evidence of haemorrhage suggested the diagnosis of GSC, whereas cystic components and pial as well as ependymal invasion were more commonly observed in GBM patients. The logistic regression model yielded a mean area under the curve (AUC) of 0.919 on the training dataset and of 0.746 on the validation dataset. The accuracy in the validation dataset was 0.67 with a sensitivity of 0.85 and a specificity of 0.5., Conclusions: Although some imaging criteria suggest the diagnosis of GSC or GBM, differentiation between these two tumour entities on standard MRI alone is not feasible.

Paragangliomas can be diagnosed accurately using magnetic resonance imaging and dynamic four-dimensional magnetic resonance angiography. Four-dimensional imaging uses the highly vascularised structure of these tumours, which results in a homogenous capillary blush and usually, due to the arteriovenous shunting, in an early draining vein. By these features the tumour can be differentiated from other neoplasms. The authors describe a case of a paraganglioma localised in the middle ear of an elderly patient. Magnetic resonance contraindications led to preoperative diagnostics with high resolution computed tomography of the temporal bone and additionally computed tomography perfusion imaging instead of magnetic resonance imaging with four-dimensional magnetic resonance angiography. Using the computed tomography perfusion dataset, regions of interest were placed in the carotid artery, the sigmoid sinus and the tympanic mass. In the computer-assisted analysis the tumour showed late arterial enhancement and delayed wash-out compared to the enhancement curves of the carotid artery and the sigmoid sinus. This corresponded to the highly vascularised nature of a paraganglioma. On postoperative follow-up imaging computed tomography perfusion showed almost no enhancement of a small residual tympanic mass, which was then considered to be granulation tissue. In conclusion, in the case of magnetic resonance contraindications the preoperative diagnosis of tympanic paraganglioma can be made using computed tomography imaging criteria alone. Computed tomography perfusion imaging may be helpful in these cases to detect residual or recurrent tumour.