Multiparametric MRI Helps Improve Discrimination of Brain Gliomas

August 18, 2014

Multiparametric MRI helps identify low- and high-grade brain gliomas, reducing risk of inappropriate or delayed surgery.

Multiparametric MR imaging significantly improved discrimination between low- and high-grade brain gliomas, according to a study published in the journal Radiology.

Researchers from Italy undertook a retrospective study to determine how multiparametric MR imaging, taking into account the heterogeneity of the lesions at MR imaging, affected current radiologic reporting methods and grading of brain gliomas.

A total of 118 patients with histologically confirmed brain gliomas were evaluated. The patients had undergone conventional and advanced MR sequences (perfusion-weighted imaging, MR spectroscopy, and diffusion-tensor imaging). Three evaluations were conducted:

  • Semiquantitative, based on conventional and advanced sequences with reported cutoffs,
  • Qualitative, exclusively based on conventional MRI, and
  • Quantitative, for which four volumes of interest were placed: regions with contrast material enhancement, regions with highest and lowest signal intensity on T2-weighted images, and regions of most restricted diffusivity.

The researchers found that there were significant differences in age, relative cerebral blood volume (rCBV) in contrast-enhanced regions (area under the ROC curve [AUC] = 0.937), areas of lowest signal intensity on T2-weighted images, restricted diffusivity regions, and choline/creatine ratio in regions with the lowest signal intensity on T2-weighted images.

“[Discriminant function analysis] (DFA) that included age; rCBV in contrast-enhanced regions, areas of lowest signal intensity on T2-weighted images, and areas of restricted diffusivity; and choline/creatine ratio in areas with lowest signal intensity on T2-weighted images was used to classify 95 percent of patients correctly,” the authors wrote. “Quantitative analysis showed a higher concordance with histologic findings than qualitative and semiquantitative methods (P < .0001).”

The researchers concluded that quantitative multiparametric MR imaging evaluation incorporating heterogeneity at MR imaging significantly improved discrimination between low- and high-grade brain gliomas with a very high AUC. This reduced the risk of inappropriate or delayed surgery.