Article Figures & Data
Figures
- Fig 1.
Multiparametric voxel-based model for infarction.
- Fig 2.
Stacked bar graph visualization of errors in ischemic core volume estimation compared with final infarction volume by Bayesian-logit and oSVD postprocessing. The error values are noticeably greater with oSVD (black bars) compared with Bayesian-logit (gray bars).
- Fig 3.
A 74-year-old woman who presented with left M1 occlusion and a baseline NIHSS score of 26. She underwent successful mechanical thrombectomy (TICI 2c). The time from CTP to recanalization was 78 minutes, and the time from CTP to MR imaging was 19 hours. The 4 CTP maps included in our final model are shown. Note that the estimated ischemic core derived from our multiparametric Bayesian-logit model provides more accurate estimation of final infarction on MR imaging in comparison with what is estimated from oSVD-CBF <30% (current clinical practice).
- Fig 4.
Bland-Altman plots of calculated final infarct volume and estimated ischemic core volume using the Bayesian-based logit score (A) and oSVD-rCBF <30% (B). Solid lines represent the mean differences. Dashed lines indicate 2 SDs above and below the mean differences. The limits of agreement were −25 to 27 for the Bayesian-based logit score and −61 to 52 for oSVD-CBF <30%.
Tables
Variable Infarction Noninfarction P Valueb TTP 32.00 (1.95) 24.30 (1.19) <.001 rCBF 24.78 (2.81) 48.43 (6.49) <.001 rCBV 2.81 (0.46) 4.09 (0.56) .02 ATD 3.58 (0.18) 1.49 (0.16) <.001 MTT 6.58 (0.58) 5.33 (0.15) .01 rCBFdiff −25.60 (10.93) 3.20 (1.20) .02 rCBVdiff −1.15 (0.52) 0.06 (0.06) .03 TTPdiff 8.39 (0.41) 0.25 (0.17) <.001 ATDdiff 2.15 (0.17) −0.16 (0.06) <.001 MTTdiff 2.19 (0.49) −0.12 (0.06) <.001 Note:—rCBV indicates relative CBV.
↵a The values are presented as mean (SD). The SDs reported are the Huber-White (robust) standard errors. All units are in seconds, except for rCBF and rCBV, which are unitless.
↵b P values are based on a linear regression model with the presence of infarct as the independent variable and imaging parameters as the outcome.
- Table 2:
Optimal threshold, sensitivity, specificity, and accuracy for TTP, rCBF, ATDdiff, MTTdiff, and the final model in identifying infarcted voxels
Variable Threshold Sensitivity Specificity Accuracy AUC TTP 28.82 seconds 65.3% 77.9% 76.5% 0.76 rCBF 22.10 60.0% 72.9% 71.5% 0.73 ATDdiff 0.87 seconds 68.1% 80.2% 78.9% 0.80 MTTdiff 1.38 seconds 56.2% 74.5% 72.5% 0.69 Final modela 0.109b 74.2% 80.0% 79.4% 0.84 ↵a The final model consisted of TTP, rCBF, ATDdiff, and MTTdiff as the independent variables and the presence of infarct as the outcome.
↵b Equation of the final model: logit(P) = −3.9170 + 0.0601 × TTP − 0.0095 × rCBF + 0.4629 × ATDdiff + 0.0989 × MTTdiff where logit(P) = estimated log odds of infarction for a given voxel. If logit(P) is greater than the optimal threshold of 0.109, the voxel is classified as infarct.
