Evidence map›Paper›PMID 40920202›Full record

ArticleNeuroradiology2025

Predicting periprocedural complications risk in intracranial angioplasty and stenting from integrated high-resolution vessel wall imaging radiomics and clinical characteristics.

Yu Guo, Chengxiu Yuan, Yuwen Su, Zhe Wang, Songchuan Li, Bao Wang, Chunhong Hu

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Neuroradiology, 2025. 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. Review
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

7 authors.

Yu Guo *Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Chengxiu Yuan *Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Yuwen SuDepartment of Radiology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Zhe WangDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Songchuan LiDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Bao WangDepartment of Radiology, Qilu Hospital of Shandong University, Jinan, China. wangbao@email.sdu.edu.cn.
Chunhong HuDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China. hch5305@163.com.

Funding

Jiangsu Province Capability Improvement Project through Science，Technology and Education （Jiangsu Provincial Medical Key Discipline Cultivation Unit） JSDW202242National Natural Science Foundation of China 82202114Suzhou Key Laboratory of Medical Imaging SZS2024032
6 · The paper itself

Abstract

purposeTo develop and validate an integrated model based on MR high-resolution vessel wall imaging (HR-VWI) radiomics and clinical features to preoperatively assess periprocedural complications (PC) risk in patients with intracranial atherosclerotic disease (ICAD) undergoing percutaneous transluminal angioplasty and stenting (PTAS).

methodsThis multicenter retrospective study enrolled 601 PTAS patients (PC+, n = 84; PC -, n = 517) from three centers. Patients were divided into training (n = 336), validation (n = 144), and test (n = 121) cohorts. All patients underwent preoperative HR-VWI (precontrast T1-weighted [T1] and postcontrast T1-weighted [T1CE] sequences). We extracted 2,396 radiomic features and selected clinical variables via multivariate logistic regression. Radiomics, clinical and integrated model were developed. Model performance was evaluated using areas under the curve (AUC) and DeLong test. Decision Curve Analysis (DCA) was used to evaluate the net benefit of each model.

resultsAge was the sole independent clinical predictor (OR = 1.06, p = 0.001). The integrated model demonstrated favorable predictive performance in the training cohort (AUC: 0.93, 95% CI [0.88, 0.96]), validation cohort (AUC: 0.87, 95% CI [0.74, 0.99]), and test cohort (AUC: 0.87, 95% CI [0.78, 0.95]). It significantly outperformed all clinical models (AUC range: 0.59-0.73; all p < 0.05) and showed performance comparable to the optimal radiomics model (T1-T1CE model; AUC range: 0.80-0.91; all p > 0.05).Notably, the DCA curve indicated that the integrated model achieved the optimal clinical net benefit across the 0-90% threshold range in the test cohort.

conclusionThe integrated model demonstrates clinical utility for preoperative PC risk stratification in PTAS patients.

Indexed as

AngioplastyIntracranial ArteriosclerosisMagnetic Resonance AngiographyPostoperative ComplicationsStentsAgedContrast MediaFemaleHumansMaleMiddle AgedPredictive Value of TestsRadiomicsRetrospective StudiesRisk AssessmentRisk FactorsContrast MediaHigh-resolution vessel wall imagingIntracranial atherosclerosis diseasePercutaneous transluminal angioplasty and stentingPeriprocedural complicationsRadiomics

Identifiers

PMID40920202

What Socratic holds

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Registered trials

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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 Socratic graph.