Evidence map›Paper›PMID 37193146›Full record

ArticleAmerican journal of translational research2023

MRI is more valuable than CT in the diagnosis of cervical cancer.

Jun Zhang, Xinlin Liu

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

2 authors at 1 institution in 1 country.

Jun ZhangDepartment of Radiology, Zhejiang Putuo Hospital Zhoushan 316100, Zhejiang, PR China.
Xinlin LiuDepartment of Radiology, Zhejiang Putuo Hospital Zhoushan 316100, Zhejiang, PR China.
Zhejiang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze the diagnostic values of CT and MRI for cervical cancer.

methodThe clinical data of 83 patients with cervical cancer and 16 patients with cervicitis admitted to Zhejiang Putuo Hospital from January 2017 to December 2021 were retrospectively analyzed. Among them, 18 patients receiving CT examination were categorized as the CT group, and the remaining 81 patients with MRI examination were the MRI group. In total, 83 patients were finally diagnosed with cervical cancer through pathologic examination. The diagnostic values of CT and MRI for cervical cancer staging and pathologic features were analyzed.

resultsCompared to CT, the sensitivity and accuracy of MRI in diagnosing cervical cancer were higher (P<0.05), as was its detection rate in the diagnosis of stage I and II (P<0.05), but the difference in the detection rate of stage III was not statistically significant. In addition, among the 83 cases of cervical cancer, it was confirmed by surgical and pathological examination that 41 cases experienced parametrial invasion, 65 had interstitial invasion, and 39 had lymph node metastasis. The detection rate of MRI in the diagnosis of interstitial and parametrial invasion was also markedly higher than that of CT (P<0.05), but the difference in the lymph node metastasis detection was not significant.

conclusionMRI can clearly display the structure of various layers of the cervix and its lesions. It is more accurate in clinical diagnosis, staging, and evaluation of pathologic features of cervical cancer compared to CT, and is available on a more reliable basis for diagnosis and treatment.

Indexed as

Cervical cancerclinical diagnosisCTMRIstaging

Identifiers

PMID37193146
PMCPMC10182514
OpenAlexW4376872383

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.