Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

Abstract

Background: This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.

Methods: A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC_50Hz and ADC_0Hz), we generated maps of the ADC change (cADC): ADC_50Hz - ADC_0Hz and the relative ADC change (rcADC): (ADC_50Hz - ADC_0Hz)/ ADC_0Hz × 100 (%).

Results: The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC_0Hz and ADC_50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10^-3mm²/s vs. 0.14 ± 0.03 × 10^-3mm²/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC_0Hz^5th (0.595; all p < 0.001).

Conclusions: The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

Keywords: Diffusion; Glioblastoma; Magnetic resonance imaging; Neoplasm metastasis.

Fig. 1

The study chart shows the inclusion and exclusion criteria and pathways for eligible patients in this study

Fig. 2

Schematic representation of the diffusion gradient waveforms, (top line) and their corresponding diffusion encoding spectrums, |F(ω)|², (bottom line) for pulsed gradient spin-echo (PGSE) (left) and oscillating gradient spin-echo (OGSE) (right). A 180° RF pulse is applied to the center of the gradient pair; therefore, the second gradient waveform acts as the opposite polarity. Δ_eff = Δ − δ/3. Δ_eff, effective diffusion time; Δ, diffusion gradient separation; δ, diffusion gradient pulse duration

Fig. 3

A 74-year-old woman with glioblastoma, isocitrate dehydrogenase-wildtype, grade 4. A contrast-enhanced T1-weighted image with a region of interest of the enhancing region (red line) (a), a FLAIR image with a region of interest of peritumoral region (orange line) (b), an apparent diffusion coefficient (ADC) map derived from pulsed gradient spin-echo (PGSE) DWI at an effective diffusion time (Δ_eff) of 44.5 ms (c), an ADC map derived from oscillating gradient spin-echo (OGSE) DWI at an Δ_eff of 7.1 ms (d), and maps of ADC change between PGSE DWI and OGSE DWI (cADC) (e) and relative ADC change between PGSE DWI and OGSE DWI (rcADC) (f). The ADC values in the tumor appear higher at short Δ_eff values than at long Δ_eff setting. Small changes in cADC and rcADC are noted between the OGSE and PGSE sequences in the tumor

Fig. 4

A 69-year-old man with a brain metastasis from colon cancer. A contrast-enhanced T1-weighted image with a region of interest of the enhancing region (red line) (a), a FLAIR image with a region of interest of peritumoral region (orange line) (b), an apparent diffusion coefficient (ADC) map derived from pulsed gradient spin-echo (PGSE) DWI at an effective diffusion time (Δ_eff) of 44.5 ms (c), an ADC map derived from oscillating gradient spin-echo (OGSE) DWI at an Δ_eff of 7.1 ms (d), and maps of ADC change between PGSE DWI and OGSE DWI (cADC) (e) and relative ADC change between PGSE DWI and OGSE DWI (rcADC) (f). The ADC values in the tumor appear higher at short Δ_eff values than at long Δ_eff setting. Large changes in cADC and rcADC are noted between the OGSE and PGSE sequences in the tumor

Fig. 5

Box-whisker plots of ADC_0Hz^mean and ADC_50Hz^mean (a), ADC_0Hz^5th and ADC_50Hz^5th (b), and ADC_0Hz^95th and ADC_50Hz^95th (c) of enhancing regions for glioblastomas and brain metastases. For each tumor, each index for ADC_50Hz was significantly higher than the corresponding index for ADC_0Hz (each p < 0.01, respectively) (a–c). Box-whisker plots of cADC^mean (d), cADC^5th (e), and cADC^95th (f) of enhancing regions for glioblastomas and brain metastases. Each index for cADC was significantly higher in brain metastases than in glioblastomas (each p < 0.01, respectively). Box-whisker plots of rcADC^mean (g), rcADC^5th (h), and rcADC.^95th (i) of enhancing regions for glioblastomas and brain metastases. Each index for rcADC was significantly higher in brain metastases than in glioblastomas (each p < 0.01, respectively). Statistical tests used: _apaired-t test, _bMann–Whitney U test

Fig. 6

Box-whisker plots of ADC_0Hz^mean and ADC_50Hz^mean (a), ADC_0Hz^5th and ADC_50Hz^5th (b), and ADC_0Hz^95th and ADC_50Hz^95th (c) of peritumoral regions for glioblastomas and brain metastases. For each tumor, each index for ADC_50Hz was significantly higher than the corresponding index for ADC_0Hz (each p < 0.01, respectively) (a–c). Box-whisker plots of cADC^mean (d), cADC^5th (e), and cADC^95th (f) of peritumoral regions for glioblastomas and brain metastases. Box-whisker plots of rcADC^mean (g), rcADC^5th (h), and rcADC.^95th (i) of peritumoral regions for glioblastomas and brain metastases. No significant difference in any of the three indices of ADC_0Hz, ADC_50Hz, cADC, and rcADC was observed between brain metastases and glioblastomas (Fig. 6a–i). Statistical tests used: _apaired-t test, _bMann–Whitney U test

Fig. 7

Receiver operating characteristic curves of the most effective indices for ADC_0Hz^5th, ADC_50Hz^95th, cADC^mean, and rcADC^mean