Evidence map›Paper›PMID 41528474›Full record

ArticleEuropean radiology2026

Role of high-density area score in predicting outcomes of large-vessel occlusion stroke after endovascular treatment.

XiaoQing Cheng, Xi Shen, SiShan Wen, JiaNan Li, LiJun Huang, QiJi Jin, Ya Liu, ChangSheng Zhou, Ping Xu, Lulu Xiao and 9 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 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

5 · Who and what money

Authors and funding

19 authors.

XiaoQing Cheng *Department of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Xi Shen *Department of Medical Imaging, Nanjing Jinling Hospital, Nanjing Medical University, Nanjing, China.
SiShan WenDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
JiaNan LiDepartment of Medical Imaging, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
LiJun HuangDepartment of Medical Imaging, Nanjing Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, China.
QiJi JinDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Ya LiuDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
ChangSheng ZhouDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Ping XuDepartment of Medical Imaging, Changzhou Children's Hospital, Affiliated Hospital of Nantong University, Jiangsu, China.
Lulu XiaoDepartment of Neurology, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
AnYu LiaoDepartment of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
ZeHong CaoDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Liang JiangDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
XinDao YinDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
ZhiQiang ZhangDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Wei XingDepartment of Radiology, Third Afliated Hospital of Soochow University, Changzhou, China.
Feng ShiDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China. feng.shi@uii-ai.com.
WuSheng ZhuDepartment of Neurology, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China. zwsemail@sina.com.ORCID http://orcid.org/0000-0003-0668-5832
GuangMing LuDepartment of Medical Imaging, Nanjing Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.

Funding

This research was funded by the National Natural Scientific Foundation of China Grant Nos 82271983
6 · The paper itself

Abstract

objectivesHigh-density areas (HDAs) are frequently observed in follow-up CT of large-vessel occlusions after endovascular therapy. Utilizing the established ASPECTS regions, incorporating the subarachnoid space and ventricles, we developed a novel HDA score to evaluate its correlation and predictive value for hemorrhagic transformations and clinical outcomes. MATERIALS AND

methodsThis retrospective, multicenter study included consecutive patients who had HDA on follow-up CT after endovascular therapy. Multivariable logistic regression and area under the receiver operating characteristic curve (AUC) analyses assessed the associations and predictive value of HDA location and score with hemorrhagic transformations and unfavorable clinical outcomes.

resultsAmong the 1130 consecutive patients treated with endovascular therapy, 542 patients (326 males; median age 70 years) had HDA were finally included. Multivariable logistic regression showed that HDA location in the lentiform nucleus (OR, 1.6; 95% CI: 1.1-2.5; p = 0.03) and ventricles (OR, 4.4; 95% CI: 1.2-16.6; p = 0.03) was associated with hemorrhagic transformations, whereas location in the lentiform nucleus (OR, 2.3; 95% CI: 1.4-3.8; p < 0.001), M1(OR, 3.9; 95% CI: 1.7-8.9; p = 0.001), and subarachnoid space (OR, 2.1; 95% CI: 1.2-3.8; p = 0.02) was associated with unfavorable clinical outcomes, as was the HDA score (OR = 1.35, 95% CI:1.19-1.54, p < 0.01). Including HDA indicators in the model significantly improved its unfavorable clinical outcome predictive power (AUC increased from 0.743 to 0.802; DeLong test; p < 0.01).

conclusionThe HDA score, which reflects the number of HDA involved regions, significantly correlated with unfavorable clinical outcomes and effectively predicted the prognosis. KEY POINTS: QuestionThe relationship between the location and extent of HDA on postoperative CT and hemorrhagic transformation and clinical outcomes in patients undergoing endovascular therapy is unclear. FindingsHemorrhagic transformation and unfavorable clinical outcomes were independently associated with the specific location of HDAs, and HDA score effectively predicted the prognosis. Clinical relevanceThe HDA score provided a simple quantitative measure that did not require specialized software. It significantly improved the prediction of unfavorable clinical outcomes and provided a risk stratification tool for patients after endovascular therapy.

Indexed as

Endovascular ProceduresStrokeTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesTreatment OutcomeComputed tomographyEndovascular therapyOutcomeStroke

Identifiers

What OpenQuestion holds

Textmetadata
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.