Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Acute Ischemic Stroke Lesion Characterization: Comparative Analysis of Cortical and Basal Ganglia Lesions Using Time-Histogram Metrics. |
| Συγγραφείς: |
Liu, Hon-Man1,2 (AUTHOR), Tseng, Wei-Lung2,3 (AUTHOR) |
| Πηγή: |
Diagnostics (2075-4418). May2026, Vol. 16 Issue 9, p1400. 9p. |
| Θεματικοί όροι: |
*Entropy, *Standard deviations, *Computer-assisted image analysis (Medicine), *Brain damage, *Diagnostic imaging, *Basal ganglia diseases, *Ischemic stroke |
| Περίληψη: |
Background and Purpose: Understanding regional differences in acute ischemic stroke (AIS) lesions, particularly between cortical and basal ganglia (BG) regions, is crucial for enhancing diagnostic precision and therapeutic strategies. This study builds upon prior research in CT-based stroke imaging by introducing entropy and SD as novel biomarkers for lesion differentiation and focuses on their temporal evolution to identify critical diagnostic windows for clinical management. Methods: We retrospectively analyzed imaging data from 45 AIS patients (27 cortical, 18 BG lesions). Time-histogram metrics—standard deviation (SD), skewness, kurtosis, and entropy—were computed for lesions in both regions across multiple time points post-stroke. Statistical comparisons utilized one-way ANOVA and post hoc t-tests (p < 0.05). Results: Cortical lesions exhibited significantly higher entropy (4.34 vs. 3.99, p = 0.000034) and SD (5.16 vs. 3.94, p = 0.000144) compared to BG lesions, reflecting greater heterogeneity. No significant differences were found in skewness or kurtosis. Peak diagnostic sensitivity occurred at 76–87 min post-stroke (p < 0.001). Temporal trends revealed increasing divergence in entropy and SD between cortical and BG lesions during this window. The identification of the 76–87-min diagnostic window offers critical insights into the hyperacute phase, where early ischemic changes are often subtle on NCCT. Conclusions: SD and entropy are robust biomarkers for distinguishing cortical and BG lesions in AIS, offering insights into regional tissue responses and temporal evolution, with potential for personalized stroke care. These metrics could serve as standalone biomarkers or be integrated into AI-based NCCT triage systems to enhance lesion characterization and therapeutic decision-making. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: |
Academic Search Index |