Evidence map›Paper›PMID 40924128›Full record

ArticleAbdominal radiology (New York)2026

Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.

Ruqi Fang, Meilin Yue, Keyi Wu, Kaili Liu, Xianying Zheng, Shuping Weng, Xiaping Chen, Yuzheng Su

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 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
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0citing papers in PubMed
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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

8 authors.

Ruqi FangDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China. fangruqi1983@163.com.
Meilin YueDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Keyi WuDepartment of Radiology, Fujian Maternity and Child Health Hospital, Wusi North Campus (Fujian Provincial Obstetrics and Gynecology Hospital), Fuzhou, China.
Kaili LiuDepartment of Radiology, Fujian Maternity and Child Health Hospital, Wusi North Campus (Fujian Provincial Obstetrics and Gynecology Hospital), Fuzhou, China.
Xianying ZhengDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Shuping WengDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Xiaping ChenDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Yuzheng SuDepartment of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.

Funding

Fujian Provincial Natural Science Foundation 2023J011215Joint Funds for the Innovation of Science and Technology, Fujian Province 2020Y9146
6 · The paper itself

Abstract

backgroundWe aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.

methodsThis retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets. Radiomic features were extracted from multiparametric magnetic resonance imaging to generate radiomic scores using a support vector machine algorithm. Three predictive models were constructed: clinical (Model

resultsThis study enrolled 283 women (training set: n = 198; test set: n = 85). The lymphovascular space invasion groups (substantial and no/focal) had significantly different radiomic scores (P < 0.05). Model

conclusionThe multiparametric magnetic resonance imaging-based radiomics machine learning nomogram showed moderate diagnostic performance for substantial lymphovascular space invasion in patients with endometrial cancer.

Indexed as

Endometrial NeoplasmsLymphatic MetastasisMultiparametric Magnetic Resonance ImagingNomogramsAdultAgedFemaleHumansMachine LearningMiddle AgedNeoplasm InvasivenessPredictive Value of TestsPreoperative CareRadiomicsRetrospective StudiesEndometrial neoplasmsLymphovascular space invasionMagnetic resonance imagingNomogramRadiomics

Identifiers

PMID40924128

What OpenQuestion holds

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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.