Abstract W P25: Anatomically-Weighted Predictive Algorithms Improve MRI-Based Tissue Outcome Predictions After Acute Ischemic Stroke

Abstract

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.