A prospective reappraisal of motor outcome prediction in patients with acute stroke by using atlas-based diffusion tensor imaging biomarkers

Background: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury. Objectives: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months. Methods: Forty patients with small acute stroke (2–7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis. Results: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0–2, n = 27) and poor-prognosis group (mRS 3–5, n = 13) by outcome. The median (25 ^th-75 ^th percentile) of MD (0.7 (0.6–0.7) vs. 0.7 (0.7–0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%–65.4%) and higher specificity (96.3% vs. 69.2%–88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters. Conclusions: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.