Evidence map›Paper›PMID 35683623›Full record

ArticleJournal of clinical medicine2022

Prediction of Carotid In-Stent Restenosis by Computed Tomography Angiography Carotid Plaque-Based Radiomics.

Xiaoqing Cheng, Zheng Dong, Jia Liu, Hongxia Li, Changsheng Zhou, Fandong Zhang, Churan Wang, Zhiqiang Zhang, Guangming Lu

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

6 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Quality assessment of radiomics models in carotid plaque: a systematic review.Quantitative imaging in medicine and surgery · 2024
    Review
  6. 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

9 authors at 2 institutions in 1 country.

Xiaoqing ChengDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210029, China.
Zheng DongDepartment of Diagnostic Radiology, Xuzhou Medical University, Xuzhou 221004, China.
Jia LiuDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210029, China.
Hongxia LiDepartment of Diagnostic Radiology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing 210000, China.
Changsheng ZhouDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210029, China.
Fandong ZhangDeepWise AI Lab, Beijing 100080, China.
Churan WangDeepWise AI Lab, Beijing 100080, China.
Zhiqiang ZhangDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210029, China.ORCID 0000-0002-3993-7330
Guangming LuDepartment of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210029, China.
Nanjing General Hospital of Nanjing Military Command · CNXuzhou Medical College · CN

Funding

National Key Research& Development Program of Ministry of Science& Technology of PR. China Grant Nos. 2018YFA0701703The Major project of the National Natural Scientific Foundation of China Grant Nos 81790653
6 · The paper itself

Abstract

In-stent restenosis (ISR) after carotid artery stenting (CAS) critically influences long-term CAS benefits and safety. The study was aimed at screening preoperative ISR-predictive features and developing predictive models. Thus, we retrospectively analyzed clinical and imaging data of 221 patients who underwent pre-CAS carotid computed tomography angiography (CTA) and whose digital subtraction angiography data for verifying ISR presence were available. Carotid plaque characteristics determined using CTA were used to build a traditional model. Backward elimination (likelihood ratio) was used for the radiomics model. Furthermore, a combined model was built using the traditional and radiomics features. Five-fold cross-validation was used to evaluate the accuracy of the trained classifier and stability of the selected features. Follow-up angiography showed ISR in 30 patients. Carotid plaque length and thickness were independently associated with ISR (multivariate analysis); regarding the conventional model, the area under the curve (AUC) was 0.84 and 0.82 in the training and validation cohorts, respectively. The corresponding AUC values for the radiomics-based model were 0.87 and 0.82, and those for the optimal combined model were 0.88 and 0.83. Plaque length and thickness could independently predict post-CAS ISR, and the combination of radiomics and plaque features afforded the best predictive performance.

Indexed as

carotid artery stentingcomputed tomography angiographyplaqueradiomicsrestenosis

Identifiers

PMID35683623
PMCPMC9180993
OpenAlexW4281765955

What OpenQuestion holds

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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.