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Purpose <p>To evaluate an artificial intelligence (AI)-assisted quantitative Collateral Score (qCS) from computed tomography angiography (CTA) for selecting mechanical thrombectomy (MT) candidates, comparing its performance to visual collateral score (vCS).</p> Methods <p>We retrospectively analyzed 118 acute ischemic stroke patients with large vessel occlusion. Patients were divided into derivation (<i>n</i> = 94) and validation (<i>n</i> = 24) cohorts. All underwent CTA and CT perfusion (CTP). qCS was derived using a U<sup>2</sup>-Net model, calculated as the vessel volume ratio between affected and healthy hemispheres. vCS was assessed using Tan and Menon scores. Infarct core volume (ICV), hypoperfusion volume (HV), mismatch volume, and mismatch ratio were calculated from CTP images. Correlations between collateral scores and perfusion parameters were analyzed. Receiver Operating Characteristic (ROC) curves determined optimal thresholds for MT selection, and DeLong test was used for performances comparison.</p> Results <p>Both qCS and vCS correlated negatively with ICV and HV (|ρ|=0.38–0.66, <i>P</i> &lt; 0.001) and positively with mismatch ratio (ρ = 0.52–0.58, <i>P</i> &lt; 0.001). Both scores significantly predicted ICV and HV (|β|=0.45–0.70, <i>P</i> &lt; 0.001), but not mismatch volume. qCS showed superior discrimination (AUC = 0.96) compared to the Tan score (AUC = 0.88, <i>P</i> &lt; 0.001). The optimal qCS threshold was 33.70%, yielding sensitivity of 0.96 and specificity of 0.83. In the validation cohort, qCS achieved the highest accuracy (0.88) and sensitivity (0.95), outperforming both Tan (0.75 and 0.75, respectively) and Menon score (0.83 and 0.90, respectively).</p> Conclusion <p>The AI-assisted qCS is an effective tool for selecting MT candidates and demonstrated better performance than traditional vCS methods, supporting its potential use in acute stroke thrombectomy triage.</p>

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Comparison of AI-assisted quantitative collateral score and visual collateral score in stroke thrombectomy triage

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Qingqing Lu,
  • Hongyi Chen,
  • Yiren Xu,
  • Qianjiang Ding,
  • Zhehao Zhang,
  • Yuning Pan

摘要

Purpose

To evaluate an artificial intelligence (AI)-assisted quantitative Collateral Score (qCS) from computed tomography angiography (CTA) for selecting mechanical thrombectomy (MT) candidates, comparing its performance to visual collateral score (vCS).

Methods

We retrospectively analyzed 118 acute ischemic stroke patients with large vessel occlusion. Patients were divided into derivation (n = 94) and validation (n = 24) cohorts. All underwent CTA and CT perfusion (CTP). qCS was derived using a U2-Net model, calculated as the vessel volume ratio between affected and healthy hemispheres. vCS was assessed using Tan and Menon scores. Infarct core volume (ICV), hypoperfusion volume (HV), mismatch volume, and mismatch ratio were calculated from CTP images. Correlations between collateral scores and perfusion parameters were analyzed. Receiver Operating Characteristic (ROC) curves determined optimal thresholds for MT selection, and DeLong test was used for performances comparison.

Results

Both qCS and vCS correlated negatively with ICV and HV (|ρ|=0.38–0.66, P < 0.001) and positively with mismatch ratio (ρ = 0.52–0.58, P < 0.001). Both scores significantly predicted ICV and HV (|β|=0.45–0.70, P < 0.001), but not mismatch volume. qCS showed superior discrimination (AUC = 0.96) compared to the Tan score (AUC = 0.88, P < 0.001). The optimal qCS threshold was 33.70%, yielding sensitivity of 0.96 and specificity of 0.83. In the validation cohort, qCS achieved the highest accuracy (0.88) and sensitivity (0.95), outperforming both Tan (0.75 and 0.75, respectively) and Menon score (0.83 and 0.90, respectively).

Conclusion

The AI-assisted qCS is an effective tool for selecting MT candidates and demonstrated better performance than traditional vCS methods, supporting its potential use in acute stroke thrombectomy triage.