Evidence map›Paper›PMID 35282103›Full record

ArticleAnnals of translational medicine2022

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer.

Liying Liu, Shuo Wang, Tao Yu, Haoyan Bai, Jingyu Liu, Danbo Wang, Yahong Luo

Abstract read
In one paragraph

Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. New progress and challenge in gynecological cancer.Annals of translational medicine · 2022
    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

7 authors.

Liying Liu *Department of Gynecology, Cancer Hospital of China Medical University, Shenyang, China.
Shuo Wang *Department of Gynecology, Cancer Hospital of China Medical University, Shenyang, China.
Tao YuDepartment of Radiology and Nuclear Medicine, Cancer Hospital of China Medical University, Shenyang, China.
Haoyan BaiDepartment of Radiology and Nuclear Medicine, Cancer Hospital of China Medical University, Shenyang, China.
Jingyu LiuDepartment of Radiology and Nuclear Medicine, Cancer Hospital of China Medical University, Shenyang, China.
Danbo WangDepartment of Gynecology, Cancer Hospital of China Medical University, Shenyang, China.
Yahong LuoDepartment of Radiology and Nuclear Medicine, Cancer Hospital of China Medical University, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the continuous progress of medical imaging technology, evaluation of cervical cancer is increasingly dependent on imaging methods. Diffusion-weighted imaging (DWI) plays an important role, and apparent diffusion coefficient (ADC) value is a unique quantitative parameter in the research of cervical cancer. Methods: In this prospective study, a total of 273 patients diagnosed with stage IB1 to IIIC1 cervical cancer based on the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging guidelines who underwent pelvic 3.0T magnetic resonance imaging (MRI), including MRI and DWI, were enrolled, and the diagnostic value of preoperative staging of cervical cancer was compared between the MRI and DWI groups. The DWI group was used to explore the potential association of mean ADC (ADC Results: The diagnostic coincidence rate of DWI was higher than that of MRI in preoperative staging of cervical cancer (χ Conclusions: The DWI is a useful tool for preoperative evaluation of cervical cancer. In local cervical lesions, ADC

Indexed as

apparent diffusion coefficient (ADC)cervical cancerDiffusion-weighted imaging (DWI)stagesupplementary therapy

Identifiers

PMID35282103
PMCPMC8848374

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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.