Orkun Sarioglu1, Fatma Ceren Sarioglu2, Ahmet Ergin Capar3, Demet Funda Bas Sokmez4, Pelin Topkaya4, Umit Belet3. 1. Department of Radiology, Health Sciences University, Tepecik Educational and Research Hospital, 35180 Yenisehir, Konak, Izmir, Turkey. orkunsarioglu@gmail.com. 2. Department of Radiology, Dokuz Eylul University School of Medicine, Izmir, Turkey. 3. Department of Radiology, Health Sciences University, Tepecik Educational and Research Hospital, 35180 Yenisehir, Konak, Izmir, Turkey. 4. Department of Neurology, Health Sciences University, Tepecik Educational and Research Hospital, Izmir, Turkey.
Abstract
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.
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 strokepatients 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.
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