Evidence map›Paper›PMID 40613113›Full record

ArticleJournal of gynecologic oncology2026

Constructing a prediction model for lymph node metastasis in patients with incidental finding of endometrial cancer based on Fully-Connected Network.

Yuzhen Huang, Qing Lin, Wei Liu, Yulan Ren, Huaying Wang, Zhiying Xu, Yu Xue, Wanying Zhou, Jiongbo Liao, Yiqin Wang and 3 more

Abstract read
In one paragraph

Article in Journal of gynecologic oncology, 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

13 authors.

Yuzhen Huang *Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0003-1244-5579
Qing Lin *Deep Neuro Cognition Lab, Institute for Infocomm Research (I2R), Agency for Science, Technology, and Research (A*STAR), Singapore.ORCID 0000-0002-3808-3492
Wei Liu *Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0001-8055-0317
Yulan Ren *Fudan University Shanghai Cancer Center, Shanghai, China.ORCID 0000-0001-9035-2218
Huaying WangFudan University Shanghai Cancer Center, Shanghai, China.ORCID 0000-0002-5027-3838
Zhiying XuObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0003-0233-9713
Yu XueObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0001-8431-2914
Wanying ZhouShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.ORCID 0009-0007-3087-324X
Jiongbo LiaoObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0002-1074-8245
Yiqin WangObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0003-1138-7741
Weimin TanShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.ORCID 0000-0002-8179-1531
Bo YanShanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.ORCID 0000-0002-5909-5855
Xiaojun ChenObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.ORCID 0000-0002-5493-1500

Funding

NKRDPC 2022YFC2704303STCSM 20Z11900700
6 · The paper itself

Abstract

objectiveRare studies focused on patients with incidental diagnosis of endometrial cancer (EC) after hysterectomy. We intended to construct a prediction model of lymph node metastasis (LNM) based on Fully-Connected Network (FC Network) for these patients.

methodsA total of 3,920 cases of EC that met the criteria from Obstetrics & Gynecology Hospital of Fudan University between January 2016 and February 2023 and 1995 cases from Fudan University Shanghai Cancer Center between January 2013 and October 2020 were retrospectively included for the construction of a predicting model which was based on FC Network. At the same time, 572 cases were prospectively collected for external validation.

resultsThe sensitivity of the model was 0.946. Lympho-vascular space invasion, myometrial invasion, tumor grade, microcystic elongated and fragmented invasion, progesterone receptor, and cancer antigen 125 were used to construct a simplified nomogram. The area under the curve of the nomogram was 0.890 and 0.885 in validation and prospective cohorts, respectively.

conclusionThe model we proposed has good sensitivity and can be used to predict the risk of LNM in patients with incidentally found EC. The simplified nomogram can be used as a substitute in certain situations. Based on another study, the threshold of 5% and 25% can be used for risk stratification.

Indexed as

Endometrial NeoplasmsLymphatic MetastasisNomogramsAdultAgedFemaleHumansHysterectomyIncidental FindingsLymph NodesMiddle AgedNeoplasm InvasivenessProspective StudiesRetrospective StudiesEndometrial CancerFully-Connected NetworkIncomplete Surgical StagingLymph Node MetastasisMachine Learning

Identifiers

PMID40613113
PMCPMC12867672

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.