Perfusion MRI differentiates treatment effect from tumor in recurrent glioblastoma
Perfusion magnetic resonance imaging (MRI)-based fractional tumor volume can differentiate treatment effect from tumor in recurrent glioblastoma, according to physicians from Stanford University Medical Center and the Shengjing Hospital of China Medical University in Shenyang. They report in the online Sept.12, 2019, edition of the AJNR American Journal of Neuroradiology that fractional tumor burden (FTB) better correlates with histologic tumor volume fraction in treated glioblastomas than other perfusion metrics, and that high and low fractional tumor burden define fractions of the contrast-enhancing lesion volume with high- and low-blood volume, respectively.
Fractional tumor burden, the volume fraction of tumor voxels above a specified relative cerebral blood volume (rCBV), is a perfusion imaging-derived metric that shows the potential to differentiate a tumor itself from treatment effect. Determining a tumor’s response to therapy has been based on the assessment of T2/FLAIR signal extent and the size of T1 gadolinium enhancement on MR imaging. However, this does not always indicate tumor progression, according to the researchers.
They evaluated the utility of quantitative FTB of the entire contrast-enhancing lesion volume in patients with suspected recurrent high-grade gliomas using two rCBV values. These were 1.0 and 1.75, which respectively have been shown in previous studies to differentiate treatment effect and to indicate aggressive tumor.
The study included 47 patients with suspected high-grade glioma recurrence who had undergone perfusion-MRI exams and subsequent surgical resection treatment at Stanford between 2007 and 2018. The MRI scans included pre- and postgadolinium axial 2D, T1-weighted spin-echo, or 3D, T1-weighted inversion recovery echo-spoiled gradient-echo BRAin VOlume (BRAVO) images. Dynamic susceptibility contrast (DSC) imaging was also performed in each patient.
The thresholds of 1.0 and 1.75 were used to define three FTB classes:
- FTBlow percentage of contrast-enhancing voxels with rCBV of less than or equal to 1.0;
- FTBmid percentage of voxels with rCBV between 1.0 and 1.75; and
- FTBhigh percentage of contrast-enhancing voxels with rCBV of 1.75 or greater.
Through a commercial software perfusion package, the authors generated mean rCBV values of the contrast-enhancing volume of interest (VOI), volumetric images of contrast-enhanced lesions superimposed on the FTB map containing colored voxels of each class, and a histogram displaying voxels for the entire contrast-enhancing VOI.
A radiation oncologist, a neurologist, and three neuro-oncologists blinded to clinical and histopathology information analyzed the imaging data, recording whether they thought the lesion in question represented treatment effect or tumor, and whether they would hypothetically change treatment on the basis of FTB.
Of the 47 patients in the study, histopathology examination of sampled tissue revealed that 30 patients had recurrent tumor and 17 had treatment effect (consisting of samples with no evidence of tumor cells and samples with scattered atypical cells). The authors reported that mean FTBlow, FTBhigh, and rCBV of the contrast-enhancing volume were significantly different between treatment effect and tumor. There was no significance found with FTBmid. Consensus was good among the five data analysts.
The authors noted that while the use of both high and low FTB percentage cut-points improved sensitivity of tumor diagnosis to 100%, specificity was low. They postulated that “FTBhigh is a more robust marker than FTBlow due to tumoral heterogeneity, in which previously treated tumors can have regional and interspersed areas of both high and low blood volume, presumably related to varying degrees of angiogenesis and necrosis, respectively.”
The authors hope their research can help physicians caring for patients with high-grade gliomas with treatment-related decision-making.
- Iv M, Liu X, Gentles AJ, et al. Perfusion-MRI-based fractional tumor burden differentiates between tumor and treatment effect in recurrent glioblastomas treatment and informs clinical decision making. AJNR Am J Neuroradiol. Published online September 12, 2019. doi: 10.3174/ajnr.A6211.