Evidence map›Paper›PMID 34335752›Full record

ArticleJournal of oncology2021

The Auxiliary Diagnosis and Imaging Characteristics of MRI Combined with CT in Patients with Cholangiocarcinoma.

Jing Li, Yuanlin Yu, Qing He

Open access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

3 authors at 1 institution in 1 country.

Jing LiDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.ORCID https://orcid.org/0000-0001-5778-9186
Yuanlin YuDepartment of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.ORCID https://orcid.org/0000-0002-5506-6934
Qing HeDepartment of Radiotherapy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.ORCID https://orcid.org/0000-0001-7027-0098
Fujian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with cholangiocarcinoma (CCA) have poor prognosis and high mortality. Therefore, early detection and early diagnosis are extremely important to control the development of CCA. This study aims to explore the diagnostic effect in patients with CCA and imaging characteristics of MRI combined with CT.

methods109 patients with suspected CCA underwent CT and MRI before diagnosis. The examination results were compared with the "gold standard." ROC curve was drawn to analyze the diagnostic efficacy of MRI combined with CT for CCA patients.

resultsThe diagnosis rate of suspected CCA patients was 95.41%. The diagnostic coincidence rate of CT and MRI examination was 89.42% and 92.31%, respectively. The diagnostic coincidence rate of MRI combined with CT examination was 100.00%. The number of CT delayed enhancement, peripheral bile duct dilatation, and hepatic capsular depression were more than those of MRI. The number of circular enhancement cases in the CT group was less than that in the MRI group. ROC curve results showed that the sensitivity and specificity of MRI combined with CT for the diagnosis of CCA patients were higher than those of MRI or CT alone.

conclusionMRI combined with CT has high diagnostic sensitivity and specificity and can provide imaging evidence for the clinical diagnosis and treatment of CCA.

Identifiers

PMID34335752
PMCPMC8315873
OpenAlexW3183941877

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.