Histogram-based analysis of cerebral blood flow using arterial spin labeling MRI in de novo brain gliomas: relationship to histopathologic grade and molecular markers

Arterial Spin Labeling Perfusion in Determining the IDH1 Status and Ki-67 Index in Brain Gliomas

The aim of the study was to evaluate the relationship between tumor blood flow (TBF) measured by the pseudo-continuous arterial spin labeling (PCASL) method and IDH1 mutation status of gliomas as well as Ki-67 proliferative index. Methods. The study included 116 patients with newly diagnosed gliomas of various grades. They received no chemotherapy or radiotherapy before MRI. IDH1 status assessment was performed after tumor removal in 106 cases—48 patients were diagnosed with wildtype gliomas (Grade 1−2—6 patients, Grade 3−4—42 patients) and 58 patients were diagnosed with mutant forms of gliomas (Grade 1−2—28 patients, Grade 3−4—30 patients). In 64 cases out of 116 Ki-67 index was measured. Absolute and normalized tumor blood flow values were measured on 3D PCASL maps. Results. TBF and normalized TBF (nTBF) in wildtype gliomas were significantly higher than in IDH1-mutant gliomas (p < 0.001). ASL perfusion showed high values of sensitivity and specificity in the differential diagnosis of gliomas with distinct IDH1 status (for TBF: specificity 75%, sensitivity 77.6%, AUC 0.783, cutoff 80.57 mL/100 g/min, for nTBF: specificity 77.1%, sensitivity 79.3%, AUC 0.791, cutoff 4.7). TBF and nTBF in wildtype high-grade gliomas (HGG) were significantly higher than in mutant forms (p < 0.001). ASL perfusion showed the following values of sensitivity and specificity in the diagnosis of mutant HGG and wildtype HGG (for TBF: specificity 83.3%, sensitivity 60%, AUC 0.719, cutoff 84.18 mL/100 g/min, for nTBF: specificity 88.1%, sensitivity 60%, AUC 0.729, cutoff 4.7). There was a significant positive correlation between tumor blood flow and Ki-67 (for TBF Rs = 0.63, for nTBF Rs = 0.61). Conclusion. ASL perfusion may be an informative factor in determining the IDH1 status in brain gliomas preoperative and tumor proliferative activity.

Mathematical model for histogram analysis of dynamic contrast-enhanced MRI: A method to evaluate the drug treatment response in rheumatoid arthritis

PURPOSE

To evaluate the effectiveness of a mathematical model for histogram analysis of DCE-MRI in distinguishing responders from non-responders during RA drug treatment.

METHOD

Twenty-three consecutive RA patients with clinically active inflammation prospectively underwent DCE-MRI at baseline and after treatment. Manual segmentation of the enhanced synovium was performed on the last phase of DCE-MRI. The voxel-based contrast enhancement was calculated in each phase to obtain 75th percentile values. Kinetic curves made from the 75th percentile values were fitted to mathematical model as follows, ΔS(t) = A(1 - e^-αt)e^-βt, where A is the upper limit of signal intensity (%), α (sec^-1) is the rate of signal increase, and β (sec^-1) is the rate of signal decrease during washout. AUC30 was calculated by integration of 30 s. SER was calculated as the signal intensity at the initial time point (t = 60) relative to the delayed time point (t = 300). The volumes of enhanced synovium (sum of the number of voxels) were also calculated.

RESULTS

After treatment, α, Aα, AUC30 and SER were significantly lower in the responder group than in the non-responder group (p = 0.033, 0.024, 0.015, and 0.007). The p value of SER was lowest. Aα, AUC30, and the volume of enhanced synovium had significantly larger changes from baseline to after treatment in the responder group than in the non-responder group (p = 0.045, 0.017, and 0.008). The volume of enhanced synovium had the lowest p value.

CONCLUSIONS

SER after treatment and change in the volume of enhanced synovium might be effective for distinguishing responders from non-responders.