Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke

Zhongping Chen; Zhenzhen Shi; Fei Lu; Linna Li; Mingyang Li; Shuo Wang; Wenxin Wang; Yongxin Li; Yu Luo; Dan Tong

doi:10.3389/fneur.2023.1170955

Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke

Front Neurol. 2023 Apr 6:14:1170955. doi: 10.3389/fneur.2023.1170955. eCollection 2023.

Authors

Zhongping Chen¹, Zhenzhen Shi¹, Fei Lu¹, Linna Li¹, Mingyang Li¹, Shuo Wang¹, Wenxin Wang², Yongxin Li³, Yu Luo⁴, Dan Tong¹

Affiliations

¹ Department of Radiology, The First Hospital of Jilin University, Changchun, China.
² Philips Healthcare, Beijing, China.
³ Neusoft Medical Systems Co., Ltd., Shenyang, Liaoning, China.
⁴ Department of Radiology, Shanghai Fourth People's Hospital, Shanghai, China.

Abstract

Purpose: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was designed for semi-quantitative assessment of early ischemic changes on non-contrast computed tomography (NCCT) for acute ischemic stroke (AIS). We evaluated two automated ASPECTS software in comparison with reference standard.

Methods: NCCT of 276 AIS patients were retrospectively reviewed (March 2018-June 2020). A three-radiologist consensus for ASPECTS was used as reference standard. Imaging data from both baseline and follow-up were evaluated for reference standard. Automated ASPECTS were calculated from baseline NCCT with 1-mm and 5-mm slice thickness, respectively. Agreement between automated ASPECTS and reference standard was assessed using intra-class correlation coefficient (ICC). Correlation of automated ASPECTS with baseline stroke severity (NIHSS) and follow-up ASPECTS were evaluated using Spearman correlation analysis.

Results: In score-based analysis, automated ASPECTS calculated from 5-mm slice thickness images agreed well with reference standard (software A: ICC = 0.77; software B: ICC = 0.65). Bland-Altman analysis revealed that the mean differences between automated ASPECTS and reference standard were ≤ 0.6. In region-based analysis, automated ASPECTS derived from 5-mm slice thickness images by software A showed higher sensitivity (0.60 vs. 0.54), lower specificity (0.91 vs. 0.94), and higher AUC (0.76 vs. 0.74) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS derived from 5-mm slice thickness images by software B showed higher sensitivity (0.56 vs. 0.51), higher specificity (0.87 vs. 0.81), higher accuracy (0.80 vs. 0.73), and higher AUC (0.71 vs. 0.66) than those using 1-mm slice thickness images (p < 0.05). Automated ASPECTS were significantly associated with baseline NIHSS and follow-up ASPECTS.

Conclusion: Automated ASPECTS showed good reliability and 5 mm was the optimal slice thickness.

Keywords: brain ischemia; computed tomography; computer-assisted; image interpretation; software validation.