Thursday, June 19, 2025

Quantitative insights into stroke recovery utilizing delayed vessel ratio from color-coded multiphase computed tomography angiography

Predicting recovery is totally fucking useless. DELIVER EXACT RECOVERY PROTOCOLS! I'd fire anyone doing prediction research, it's useless!

 Quantitative insights into stroke recovery utilizing delayed vessel ratio from color-coded multiphase computed tomography angiography


Yu Lin1,2,3Xiaoxiao Zhang2Zhen Xing1Xiefeng Yang1Qingwen Tong4Shaomao Lv2Jinan Wang2,3 and Dairong Cao1,5,6,7*

1Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China

2Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, School of Clinical Medicine of Fujian Medical University, Xiamen, China

3Xiamen Radiology Quality Control Center, Zhongshan Hospital Affiliated to Xiamen University, School of Clinical Medicine of Fujian Medical University, Xiamen, China

4Department of Health Examination, Xiamen Humanity Hospital Fujian Medical University, Xiamen, China

5Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital of Fujian Medical University, Fuzhou, China

6Fujian Provincial Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China

7Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China

Edited by
Alan Wang, The University of Auckland, New Zealand

Reviewed by
Rodrigo Assar, University of Chile, Chile
Jihoon Kang, Seoul National University Bundang Hospital, Republic of Korea

*Correspondence
Dairong Cao, dairongcao@163.com

These authors have contributed equally to this work and share first authorship

Received 30 January 2025
Accepted 03 June 2025
Published 18 June 2025

Citation
Lin Y, Zhang X, Xing Z, Yang X, Tong Q, Lv S, Wang J and Cao D (2025) Quantitative insights into stroke recovery utilizing delayed vessel ratio from color-coded multiphase computed tomography angiography. Front. Neurol. 16:1568717. doi: 10.3389/fneur.2025.1568717

Background and objective: 

The color-coded multiphase computed tomography angiography (cmCTA) is an accredited technique that employs color-coding to visually depict the temporal dynamics of collateral blood flow in patients with acute ischemic stroke (AIS). This research aimed to assess the quantification of cmCTA in AIS patients for characterizing arterial and venous collateral flow, and predicting functional outcomes.

Methods: 

A retrospective study was performed on a consecutive cohort of AIS patients with large vessel occlusion who underwent cmCTA scan and reconstruction. Collateral ratio and delayed vessel ratio (DVR) were determined through semi-automatic delineation and calculation on the anterior cerebral artery regions and Alberta Stroke Program Early CT (ASPECT) Score regions of cmCTA maps. Deep venous outflow (DVO) and superficial venous outflow (SVO) scores were assessed using a 6-point scale. Logistic regression and propensity score were applied to confounding factors adjustment and model construction. Receiver operating characteristic curve, calibration curve, and decision curve analysis were utilized to evaluate the prediction model of functional independence and excellent recovery.

Results: 

Well-developed arterial collaterals as depicted by low DVR and adequate venous collaterals as indicated by high DVO or SVO were correlated with better outcomes (All p < 0.001). Adjusted DVR showed areas under the curve of 0.81–0.90 for predicting functional independence and excellent recovery. Adjusted DVO showed areas under the curve of 0.88 for predicting functional independence and excellent recovery. Each prediction model demonstrated good precision and net benefit.

Conclusion: 

The application of DVR and other parameters in cmCTA offers a quantitative perspective on the conventional ASPECT scoring scheme utilizing grayscale CT images. DVR from cmCTA may enhance pre-treatment collateral assessment and post-treatment outcome prediction in AIS, facilitating informed treatment decisions.

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