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. 2023 May 25:17:1177044.
doi: 10.3389/fnins.2023.1177044. eCollection 2023.

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke

Rui He  1 , Jia Zhou  1 , Xiaoyu Xu  1 , Xiaoer Wei  1 , Feng Wang  2 , Yuehua Li  1
Affiliations

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke

Rui He et al. Front Neurosci. .

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Abstract

Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6 weeks) to V5 (7 months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRMTmax-, PRMrCBV-, and PRMrCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.

Keywords: ischemic stroke; parametric response mapping; perfusion MRI; prediction; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the patient enrollment. NIHSS indicates the National Institutes of Health Stroke Scale; mRS: modified Rankin Scale; MRI, magnetic resonance imaging.
Figure 2
Figure 2
Description of the PRM analysis. Step 1: MRI data undergo digital image postprocessing that involves co-registration of images for each pair of time points; Step 2: The 95% CI for the classification of parametric values is computed based on the lesion-mirrored ROI in the healthy hemisphere. The parametric threshold is determined by the 95% unchanged CI resulting from linear least squares analysis on the combined data from all samples; Step 3: PRM maps are determined by calculating the difference between parametric values within the lesion at the pair of time points. PRM maps appear as color-coded overlays on the original parametric maps. Areas with unchanged parametric values are in green, increased values are in red, and decreased values are in blue. The scatter plot represents the two coordinates of a spot and is the parametric value of the same pixel at two-time points. ROI indicates an area of interest; CI, confidence interval; PRM, parametric response mapping; MRI, magnetic resonance imaging.
Figure 3
Figure 3
Maps of FLAIR, MTT, TTP, Tmax, rCBF, rCBV at V2-V5, and PRM color-coded maps overlays on MTT, TTP, Tmax, rCBF, rCBV maps obtained in a representative patient of the poor-outcome subgroup. Each PRM map corresponds to the comparison at V3-V5 with V2. The scale of signal intensity and the threshold for PRM stratification for each map were noted at the end of each column. FLAIR indicates fluid attenuation inversion recovery; MTT, mean transit time; TTP, time-to-peak; Tmax, time-to-maximum; rCBF, relative cerebral blood flow; rCBV, relative cerebral blood volume; PRM, parametric response mapping; L, left hemisphere; R, right hemisphere.
Figure 4
Figure 4
The PRMrCBV color-coded overlay, the corresponding quantitative scatter plot, and the histogram of a representative patient in the good-outcome subgroup compared to the representations of one in the poor-outcome subgroup. (A,E) The regions in which rCBV values were significantly increased (red voxels), unchanged (green voxels), or significantly decreased (blue voxels) based on the predetermined threshold (CI = 1.0%) were represented in a color-coded overlay. (B,D) The scatter plots showed the distribution of rCBV at V2 and V3 for the entire 3-D lesion volume. The 95% CIs within the scatter plot were designated by two black dashed lines. (C,F) The histogram represented the grouped frequency distribution of rCBV. PRM indicates parametric response mapping; CI, confidence interval; rCBV, relative cerebral blood volume.
Figure 5
Figure 5
Receiver operating characteristic (ROC) curve of PRM-derived parameters and mean values for predicting the final mRS score. The best diagnostic performance of PRM-derived parameters for predicting the mRS at V5 could be achieved with PRMrCBV- at V3 with 7.0% as the cutoff value (AUC: 0.951; sensitivity: 0.88; specificity: 1.00). PRMrCBV+ and PRMTmax- at V3 also performed well in predicting the mRS at V5. The dashed line indicates a curve with no discrimination. ROC indicates receiver-operating characteristic; PRM, parametric response mapping; mRS, modified Rankin Scale; AUC, area under the curve; ILV, ischemic lesion volume; rCBV, relative cerebral blood volume; Tmax, time-to-maximum.
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