Brain SPECT and perfusion MRI: do they provide complementary information about the tumour lesion and its grading?. Issue 8 (August 2019) - Daboudi, M.
Abstract : AIM: To evaluate the relative and combined utility of 99m Tc-tetrofosmin ( 99m Tc-TF) brain single-photon-emission computed tomography (SPECT) and dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) in grading brain gliomas. MATERIALS AND METHODS: Thirty-six patients with clinically suspected brain tumours were assessed by 99m Tc-TF SPECT and DSC-MRI. Brain tumour malignancy was confirmed in all patients at histopathology. On both techniques brain lesions were evaluated via visual and semi-quantitative analysis methods (deriving tetrofosmin index [T-index] and relative cerebral blood volume [rCBV] ratios, respectively). RESULTS: 99m Tc-TF SPECT showed abnormally elevated tracer uptake in 31/36 patients whereas MRI detected the brain tumour in all patients. Optimal cut-off values of each index for discriminating between low- and high-grade gliomas were obtained through receiver operating characteristic (ROC) analyses. A T-index cut-off of 6.35 ensured 82% sensitivity and 71% specificity for discriminating between high- and low-grade gliomas, whereas a relative rCBV ratio cut-off of 1.80 achieved 91% sensitivity and 100% specificity. Requiring a positive result on either technique to characterise a high-grade glioma was associated with similar specificity and slightly increased sensitivity. CONCLUSION: Both imaging techniques, 99m TF SPECT and DSC MRI, may provide complementary indices of tumour grade and have an independent diagnostic value for high-risk tumours. Highlights: Evaluation of 99mTc TFSPECT imaging and DSC MRI in providing complementary information regarding tumor grading. DSC MRI rCBV ratio and 99mTcTF SPECT T-index value varied significantly as a function of tumor grade. T-index cutoff value of 6.35: 82% sensitivity and 71% specificity in discriminating high vs low grade gliomas. rCBV ratio cutoff of 1.80: 91% sensitivity and 100% specificity. Combining data from the two methods improves sensitivity.