Diagnostic performance of single-phase CT angiography in detecting large vessel occlusion in ischemic stroke: A systematic review

Correlation between Hypoperfusion Intensity Ratio and Functional Outcome in Large-Vessel Occlusion Acute Ischemic Stroke: Comparison with Multi-Phase CT Angiography

Background and purpose: Previous studies have shown that Hypoperfusion Intensity Ratio (HIR) derived from Perfusion Imaging (PWI) associated with collateral status in large-vessel occlusion (LVO) acute ischemic stroke (AIS) and could predict the rate of collateral flow, speed of infarct growth, and clinical outcome after endovascular treatment (EVT). We hypothesized that HIR derived from CT Perfusion (CTP) imaging could relatively accurately predict the functional outcome in LVO AIS patients receiving different types of treatment. Methods: Imaging and clinical data of consecutive patients with LVO AIS were retrospectively reviewed. Multi-phase CT angiography (mCTA) scoring was performed by 2 blinded neuroradiologists. CTP images were processed using an automatic post-processing analysis software. Correlation between the HIR and the functional outcome was calculated using the Spearman correlation. The efficacy of the HIR and the CTA collateral scores for predicting prognosis were compared. The optimal threshold of the HIR for predicting favorable functional outcome was determined using receiver operating characteristic (ROC) curve analysis. Results: 235 patients with LVO AIS were included. Patients with favorable functional outcome had lower HIR (0.1 [interquartile range (IQR), 0.1−0.2]) vs. 0.4 (IQR, 0.4−0.5)) and higher mCTA collateral scores (3 [IQR, 3−4] vs. 3 [IQR, 2−3]; p < 0.001) along with smaller infarct core volume (2.1 [IQR, 1.0−4.5]) vs. (15.2 [IQR, 5.5−39.3]; p < 0.001), larger mismatch ratio (22.9 [IQR, 11.6−45.6]) vs. (5.8 [IQR, 2.6−14]); p < 0.001), smaller ischemic volume (59.0 [IQR, 29.7−89.2]) vs. (97.5 [IQR, 68.7−142.2]; p < 0.001), and smaller final infarct volume (12.6 [IQR, 7.5−18.4]) vs. (78.9 [IQR, 44.5−165.0]; p < 0.001) than those with unfavorable functional outcome. The HIR was significantly positively correlated with the functional outcome [r = 0.852; 95% confidence interval (CI): 0.813−0.884; p < 0.0001]. The receiver operating characteristic (ROC) analysis showed that the optimal threshold for predicting a favorable functional outcome was HIR ≤ 0.3 [area under the curve (AUC) 0.968; sensitivity 88.89%; specificity 99.21%], which was higher than the mCTA collateral score [AUC 0.741; sensitivity 82.4%; specificity 48.8%]. Conclusions: HIR was associated with the functional outcome of LVO AIS patients, and the correlation coefficient was higher than mCTA collateral score. HIR outperformed mCTA collateral score in predicting functional outcome.

Artificial intelligence software for diagnosing intracranial arterial occlusion in patients with acute ischemic stroke

PURPOSE

To evaluate the diagnostic performance of AI software in diagnosing intracranial arterial occlusions in the proximal anterior circulation at CT angiography (CTA) and to compare it to manual reading performed in clinical practice.

METHODS

Patients with acute ischemic stroke underwent CTA to detect arterial occlusion in the proximal anterior circulation. Retrospective review of CTA scans by two neuroradiologists served as reference standard. Sensitivity and specificity of AI software (StrokeViewer) were compared to those of manual reading using the McNemar test. The proportions of correctly detected occlusions in the distal internal carotid artery and/or M1 segment of the middle cerebral artery (large vessel occlusion [LVO]) and in the M2 segment of the middle cerebral artery (medium vessel occlusion [MeVO]) were calculated.

RESULTS

Of the 474 patients, 75 (15.8%) had an arterial occlusion in the proximal anterior circulation according to the reference standard. Sensitivity of StrokeViewer software was not significantly different compared to that of manual reading (77.3% vs. 78.7%, P = 1.000). Specificity of StrokeViewer software was significantly lower than that of manual reading (88.5% vs. 100%, P < 0.001). StrokeViewer software correctly identified 40 of 42 LVOs (95.2%) and 18 of 33 MeVOs (54.5%). StrokeViewer software detected 8 of 16 (50%) intracranial arterial occlusions which were missed by manual reading.

CONCLUSION

The current AI software detected intracranial arterial occlusion with moderate sensitivity and fairly high specificity. The AI software may detect additional occlusions which are missed by manual reading. As such, the use of AI software may be of value in clinical stroke care.