Evidence map›Paper›PMID 41912970›Full record

ArticleInsights into imaging2026

Dual-energy CT clot and peri-clot radiomics for predicting complete reperfusion and clinical outcome following endovascular therapy in acute ischemic stroke.

Bi-Cong Yan, Li-Na Shi, Yi-Jia Xiong, Lin-Hui Zhao, Kai Sheng, Meng-Fei Wang, Zhuo Li, Bao-Hui Guan, Jing-Xuan Jiang, Yue-Hua Li

Abstract read
In one paragraph

Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bi-Cong Yan *Department of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Li-Na Shi *Department of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Yi-Jia XiongDepartment of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Lin-Hui ZhaoDepartment of Radiology, Renji Hospital, Affiliated with Shanghai Jiao Tong University, Shanghai, China.
Kai ShengDepartment of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Meng-Fei WangSchool of Computer Science and Technology, Donghua University, Shanghai, China.
Zhuo LiShenyang University of Technology, Shenyang, China.
Bao-Hui GuanDepartment of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.
Jing-Xuan JiangDepartment of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China. jjx891103@163.com.
Yue-Hua LiDepartment of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital, Shanghai, China.

Funding

National Natural Science Foundation of China No.82402364Pudong New Area Science and Technology Development Fund PKJ2023-Y53
6 · The paper itself

Abstract

objectivesComplete reperfusion is the optimal technical goal of endovascular therapy (EVT) and is closely linked to favorable outcomes in acute ischemic stroke (AIS). This study developed and validated clot- and peri-clot-based radiomics models on pre-interventional dual-energy CT angiography (DE-CTA) to predict complete reperfusion and clinical outcome after EVT. MATERIALS AND

methodsA total of 371 patients from three centers were retrospectively enrolled and assigned to training (n = 154), test (n = 66), and validation (n = 151) cohorts. Radiomics features from clot and peri-clot regions were extracted, and three machine learning models-clot-based, peri-clot-based, and combined-were constructed. Model performance for predicting complete reperfusion and 90-day outcome was assessed using AUC.

resultsSmall, optimized feature subsets were selected for each model (11/11, 17/10, and 13/10 features for clot-based, peri-clot-based, and combined models for reperfusion and outcome prediction, respectively). For complete reperfusion, the peri-clot model showed the best performance with AUCs of 0.885 (95% CI: 0.834-0.937), 0.860 (95% CI: 0.771-0.948), and 0.847 (95% CI: 0.778-0.916) in the training, test, and validation cohorts, outperforming the clot-based (0.809, 0.759, 0.719) and combined models (0.867, 0.840, 0.820). A similar advantage was observed for outcome prediction, where the peri-clot model achieved the highest AUCs (0.854, 0.817, 0.850), exceeding the combined (0.839, 0.763, 0.804) and clot-based models (0.826, 0.709, 0.734).

conclusionsDE-CTA peri-clot radiomics provides superior prediction of both complete reperfusion and functional outcome after EVT, underscoring the clinical relevance of peri-clot microenvironment imaging and its potential to enhance pre-EVT patient selection and individualized prognostic evaluation.

trial registrationThe trial registration number (Chinese Clinical Trial Registry, ChiCTR2400092800) and date of registration (2024.11.22) were retrospectively registered. CRITICAL RELEVANCE STATEMENT: Peri-clot dual-energy computed tomography angiography radiomics outperformed clot-based models in multicenter external validation for predicting complete reperfusion and good 90-day functional outcome after endovascular therapy for acute ischemic stroke, supporting improved preprocedural risk stratification and patient selection. KEY POINTS: Complete reperfusion and functional recovery after endovascular thrombectomy remain hard to predict using thrombus features alone. Peri-clot radiomics on dual-energy computed tomography angiography achieved an external validation performance of 0.847 for complete reperfusion prediction. The same peri-clot model predicted 90-day functional outcome with 0.850 performance, supporting microenvironment-informed risk stratification. Peri-clot signatures consistently outperformed clot-only and combined models across multicenter cohorts, supporting robustness.

Indexed as

Dual-energy CTEndovascular therapyRadiomicsStroke

Identifiers

PMID41912970
PMCPMC13036058

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.