Evidence map›Paper›PMID 42543756›Full record

ArticleKorean journal of radiology2026

Preoperative Prediction of Lymphovascular Space Invasion in Endometrial Cancer Using Diffusion MRI-Derived Vessel Density.

Jiale Wang, Yifan Wang, Li Mei Guo, Xiaoxiao Zhang, Jinliang Niu, Xiao-Li Song

Abstract read
In one paragraph

Article in Korean journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Jiale Wang *Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China.ORCID https://orcid.org/0009-0002-3191-3630
Yifan Wang *Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China.ORCID https://orcid.org/0009-0003-3346-4266
Li Mei GuoDepartment of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China.ORCID https://orcid.org/0009-0001-8739-166X
Xiaoxiao ZhangPhilips Healthcare, C&TS, Beijing, China.ORCID https://orcid.org/0009-0000-9628-6514
Jinliang NiuDepartment of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China. sxlscjy@163.com.ORCID https://orcid.org/0000-0002-4465-5477
Xiao-Li SongDepartment of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, China. songxiaoli99@126.com.ORCID https://orcid.org/0000-0002-9868-7837

Funding

Applied Basic Research Programs of Shanxi Province 202503021211280National Natural Science Foundation of China 82502461
6 · The paper itself

Abstract

objectiveTo evaluate the feasibility of diffusion-derived vessel density (DDVD) for the preoperative prediction of lymphovascular space invasion (LVSI) in endometrial cancer (EC) and to explore whether the integration of DDVD with intravoxel incoherent motion (IVIM)-derived parameters and clinicopathologic (CP) variables could improve the prediction. MATERIALS AND

methodsThis exploratory retrospective study included 194 patients with histopathologically confirmed EC (126 LVSI-negative and 68 LVSI-positive) who underwent preoperative multi-b-value diffusion-weighted magnetic resonance imaging. The apparent diffusion coefficient, true diffusion coefficient (

resultsThe

conclusionDDVD

Indexed as

Diffusion Magnetic Resonance ImagingEndometrial NeoplasmsAdultAgedFeasibility StudiesFemaleHumansLymphatic MetastasisMiddle AgedNeoplasm InvasivenessPredictive Value of TestsPreoperative CareRetrospective StudiesROC CurveDiffusion-derived vessel densityDiffusion-weighted imagingEndometrial cancerLymphovascular space invasionMagnetic resonance imaging

Identifiers

PMID42543756
PMCPMC13437214

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.