Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging

Abstract

Background and purpose: Glioblastomas, brain metastases, and PCLs may have similar enhancement patterns on MR imaging, making the differential diagnosis difficult or even impossible. The purpose of this study was to determine whether a combination of DTI and DSC can assist in the differentiation of glioblastomas, solitary brain metastases, and PCLs.

Materials and methods: Twenty-six glioblastomas, 25 brain metastases, and 16 PCLs were retrospectively identified. DTI metrics, including FA, ADC, CL, CP, CS, and rCBV were measured from the enhancing, immediate peritumoral and distant peritumoral regions. A 2-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.

Results: From the enhancing region, significantly elevated FA, CL, and CP and decreased CS values were observed in glioblastomas compared with brain metastases and PCLs (P < .001), whereas ADC, rCBV, and rCBV(max) values of glioblastomas were significantly higher than those of PCLs (P < .01). The best model to distinguish glioblastomas from nonglioblastomas consisted of ADC, CS (or FA) from the enhancing region, and rCBV from the immediate peritumoral region, resulting in AUC = 0.938. The best predictor to differentiate PCLs from brain metastases comprised ADC from the enhancing region and CP from the immediate peritumoral region with AUC = 0.909.

Conclusions: The combination of DTI metrics and rCBV measurement can help in the differentiation of glioblastomas from brain metastases and PCLs.

Fig 1.

Hierarchic tree classification scheme.

Fig 2.

A 71-year-old man with a glioblastoma in the left thalamus. A, Axial contrast-enhanced T1-weighted image shows solid enhancement. B, FLAIR image demonstrates hyperintense abnormalities, extending from the thalamus to the occipital lobe (not shown at this section level). C, CBV map demonstrates elevated blood volume of the enhancing part (rCBV_max = 6.52). D, ADC map shows restricted diffusion of the enhancing part (0.75 × 10⁻³/mm²/s). E−G, FA (E), CL (F), and CP (G) from the enhancing part (0.18, 0.15, and 0.15, respectively) are higher than those for brain metastasis (not shown) and PCL (not shown). H, CS from the enhancing portion (0.68) is lower compared with brain metastasis and PCL.

Fig 3.

Boxplots of diffusion (A and B) and perfusion (C) characteristics in brain metastases (gray), glioblastomas (white), and PCLs (dotted). The solid line inside the box represents the median value, while the edges represent the 25th and 75th percentiles. Straight line (bar) on each box indicates the range of data distribution. Circles represent outliers (values >1.5 box length from the 75^th and 25th percentile). The asterisk above the gray or dotted box indicates a significant difference (P < .05) for glioblastomas versus metastases or glioblastomas versus PCLs, respectively. The asterisk above a horizontal line between gray and dotted boxes indicates a significant difference (P < .05) between metastases and PCLs.

Fig 4.

ROC curves of the imaging parameters with high predictive power from the enhancing part as well as the LRM for levels 1 (A) and 2 (B) of decision tree steps (Fig 1). LRM of ADC, CS from ER, and rCBV from the IPR were the best predictors for differentiation of glioblastomas from nonglioblastomas with AUC = 0.938 (A), whereas a combination of ADC from the ER and CP from the IPR was the best model for distinguishing lymphomas from metastases with AUC = 0.909 (B).

Fig 5.

Scatterplot of FA and CS from the enhancing region of the glioblastomas (blue square) and nonglioblastomas (purple square). There is a strong negative correlation between FA and CS (r = 0.99).