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

Autor: Sarioglu O; Department of Radiology, Health Sciences University, Tepecik Educational and Research Hospital, 35180 Yenisehir, Konak, Izmir, Turkey. orkunsarioglu@gmail.com., Sarioglu FC; Department of Radiology, Dokuz Eylul University School of Medicine, Izmir, Turkey., Capar AE; Department of Radiology, Health Sciences University, Tepecik Educational and Research Hospital, 35180 Yenisehir, Konak, Izmir, Turkey., Sokmez DFB; Department of Neurology, Health Sciences University, Tepecik Educational and Research Hospital, Izmir, Turkey., Topkaya P; Department of Neurology, Health Sciences University, Tepecik Educational and Research Hospital, Izmir, Turkey., Belet U; Department of Radiology, Health Sciences University, Tepecik Educational and Research Hospital, 35180 Yenisehir, Konak, Izmir, Turkey.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2021 Aug; Vol. 31 (8), pp. 6105-6115. Date of Electronic Publication: 2021 Feb 09.
DOI: 10.1007/s00330-021-07720-4
Abstrakt: 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.
Key Points: • The gray-level run-length matrix parameters displayed higher diagnostic performance among the texture features. • The long-run high gray-level emphasis showed the highest sensitivity and specificity rates for predicting a bad outcome in stroke patients undergoing mechanical thrombectomy. • The gray-level run-length matrix_long-run high gray-level emphasis value of > 4885.0 (OR = 11.06; 95% CI = 2.51 - 48.77; p = 0.001) and the time from onset to puncture of > 237.5 min (OR = 8.55; 95% CI = 1.96 - 37.21; p = 0.004) were found as independent predictors of the bad outcome.
(© 2021. European Society of Radiology.)
Databáze: MEDLINE
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