The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy

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Sarioglu O., Sarioglu F. C., Capar A. E., Sokmez D. F. B., Topkaya P., Belet U.

EUROPEAN RADIOLOGY, cilt.31, sa.8, ss.6105-6115, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 8
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00330-021-07720-4
  • Dergi Adı: EUROPEAN RADIOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Biotechnology Research Abstracts, CINAHL, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.6105-6115
  • Anahtar Kelimeler: Stroke, Thrombectomy, Tomography, Computer-assisted image analysis
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Objectives To evaluate the performance of CT-based texture analysis (TA) for predicting clinical outcomes of mechanical thrombectomy (MT) in acute ischemic stroke (AIS). Methods This single-center, retrospective study contained 64 consecutive patients with AIS who underwent MT for large anterior circulation occlusion between December 2016 and January 2020. Patients were divided into 2 groups according to the modified Rankin scale (mRS) scores at 3 months as good outcome (mRS <= 2) and bad outcome (mRS > 2). Two observers examined the early ischemic changes for TA on baseline non-contrast CT images independently. Demographic, clinical, periprocedural, and texture variables were compared between the groups and ROC curves were made. Logistic regression analysis was used and a model was created to determine the independent predictors of a bad outcome. Results Sixty-four patients (32 female, 32 male; mean age 63.03 +/- 14.42) were included in the study. Fourteen texture parameters were significantly different between patients with good and bad outcomes. The long-run high gray-level emphasis (LRHGE), which is a gray-level run-length matrix (GLRLM) feature, showed the highest sensitivity (80%) and specificity (70%) rates to predict disability. The GLRLM_LRHGE value of > 4885.0 and the time from onset to puncture of > 237.5 mi were found as independent predictors of the bad outcome. The diagnostic rate was 80.0% when using the combination of the GLRLM_LRHGE and the time from onset to puncture cutoff values. Conclusion CT-based TA might be a promising modality to predict clinical outcome after MT in patients with AIS.