Abstract W P25: Anatomically-Weighted Predictive Algorithms Improve MRI-Based Tissue Outcome Predictions After Acute Ischemic Stroke
Background: In acute ischemic stroke (AIS) patients, multi-parametric MRI-based predictive algorithms have shown promise in identifying tissue at risk of infarction, but do not consider the intrinsic variations of normal or pathological tissue. We hypothesized that extending MRI-based algorithms to take into consideration tissue type will improve predictions of tissue outcome.
Methods: We retrospectively analyzed AIS patients who received neither revascularization nor experimental interventional treatment, who underwent MRI within 12 h from the time since they were last known well and who had follow-up imaging >4 days. Perfusion- and diffusion-parametric maps were combined to predict tissue outcome using 2 models: 1) a generalized linear model (GLM) trained with data from the whole ipsilateral hemisphere (sGLM), irrespective of tissue type, or 2) an anatomically-weighted GLM (aGLM) that was calculated using a weighted average to combine results from models generated using entire white or gray matter regions only. Both methods were evaluated using jack-knifing and predicted and follow-up regions were compared in terms of accuracy (measured as area under the receiver operator characteristic curve, AUC), Dice similarity index (DSI) and root mean square error (RMSE).
Results: Results from 109 patients (65% male, median 68 y IQR [55-77], NIHSS 14 [9-25]) showed that, compared to sGLM, aGLM’s predictions had higher DSI (0.48 [0.19-0.59], P<0.001), and AUC (0.89 [0.86-0.94], P=0.001) and lower RMSE (0.32 [0.29-0.35], P<0.001), all demonstrating improved performance.
Discussion: We showed that anatomically-weighted algorithms may better capture differences in tissue vulnerability in acute ischemic stroke, contributing to improved MRI-based tissue outcome predictions.
Author Disclosures: M.J.R.J. Bouts: Research Grant; Significant; NIH/NINDS P50NS051343, R01-NS059775, R01-NS082285. E. McIntosh: Research Grant; Modest; U01NS069208. Research Grant; Significant; R01NS063925. R. Bezerra: Research Grant; Significant; R01NS063925. I. Diwan: Research Grant; Significant; R01NS063925, R01NS059775. S.J.T. Mocking: Research Grant; Modest; U01NS069208. Research Grant; Significant; R01NS063925, R01NS059775. P. Garg: Research Grant; Modest; R01NS063925. W.T. Kimberly: None. E. Arsava: None. W.A. Copen: Research Grant; Modest; R01NS059775. P.W. Schaefer: None. H. Ay: Research Grant; Modest; R01NS063925. A.B. Singhal: Research Grant; Modest; R01NS059775. O. Wu: Research Grant; Modest; NIH/NINDS U01NS069208. Research Grant; Significant; NIH/NINDS P50NS051343, R01NS059775, R01NS063925, R01NS082285. Consultant/Advisory Board; Modest; Penumbra, Inc. Ownership Interest;. Consultant/Advisory Board; Significant; Co-inventor US Patent 7,020,578. 2006 Mar 28.
- © 2015 by American Heart Association, Inc.