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Validation of two automated ASPECTS software on non-contrast computed tomography scans of patients with acute ischemic stroke.
Chen, Zhongping; Shi, Zhenzhen; Lu, Fei; Li, Linna; Li, Mingyang; Wang, Shuo; Wang, Wenxin; Li, Yongxin; Luo, Yu; Tong, Dan.
Affiliation
  • Chen Z; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Shi Z; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Lu F; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Li L; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Li M; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Wang S; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
  • Wang W; Philips Healthcare, Beijing, China.
  • Li Y; Neusoft Medical Systems Co., Ltd., Shenyang, Liaoning, China.
  • Luo Y; Department of Radiology, Shanghai Fourth People's Hospital, Shanghai, China.
  • Tong D; Department of Radiology, The First Hospital of Jilin University, Changchun, China.
Front Neurol ; 14: 1170955, 2023.
Article in En | MEDLINE | ID: mdl-37090971
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Neurol Year: 2023 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Neurol Year: 2023 Document type: Article Affiliation country: China
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