Evidence map›Paper›PMID 41589874›Full record

ArticleMenopause (New York, N.Y.)2026

Deciphering the predictors of endometrial nonbenign lesions in asymptomatic postmenopausal women via explainable machine learning.

Linlin Yang, Chen Xu, Rongjia Su, Xiaoyan Gao, Baoying Ye, Yuan Liu, Jing Wang, Yidan Chen, Qiong Fan, Jiangjing Yuan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Menopause (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Linlin YangDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Chen XuDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Rongjia SuDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xiaoyan GaoDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Baoying YeDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Municipal Key Clinical Specialty, Shanghai, China.
Yuan LiuDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Municipal Key Clinical Specialty, Shanghai, China.
Jing WangDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yidan ChenDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qiong FanDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jiangjing YuanDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xiaojing LuDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Dongjian YangClinical Research Center, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yuhong LiDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yudong WangDepartment of Gynecological Oncology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTimely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for asymptomatic endometrial thickening. The aim of this study was to develop a strong machine learning (ML) model for assessing the risk of endometrial malignancy in asymptomatic patients after menopause.

methodsThis retrospective study was designed to collect data from 971 postmenopausal asymptomatic women with endometrial thickening. The bootstrap resampling method was used for model training, internal validation, and external validation. With 41 easily accessible characteristics, multifactor regression and least absolute shrinkage and selection operator regression were performed for feature selection. Nine ML algorithms were applied to build a model. To explain the final model and rank feature importance, the SHapley Additive exPlanation (SHAP) method was utilized. Meanwhile, a nomogram was developed to facilitate model interpretation.

resultsThe comprehensive methodologies identified parity, Doppler flow signals, endometrial thickness, cancer antigen 125, and D-dimer as significant predictors. The logistic regression (LR) model demonstrated superior performance compared with other ML algorithms, achieving an accuracy of 88%, a sensitivity of 78%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.81. Furthermore, individualized predictions of endometrial malignancy were visualized through a force plot generated by SHAP analysis. A nomogram based on the LR model was subsequently constructed, showing area under the receiver operating characteristic curve values of 0.82, 0.82, and 0.81 for the training, internal validation, and external validation cohorts, respectively. The calibration curve demonstrated excellent consistency.

conclusionsWe developed an LR-based nomogram model and interpreted using the SHAP method, which provided visual insights for detecting endometrial nonbenign lesions in asymptomatic postmenopausal women. This approach would aid clinicians in providing individualized treatment and help avoid unnecessary invasive surgeries.

Indexed as

Endometrial NeoplasmsEndometriumMachine LearningPostmenopauseAgedAlgorithmsCA-125 AntigenFemaleFibrin Fibrinogen Degradation ProductsHumansLogistic ModelsMiddle AgedNomogramsParityPrediction AlgorithmsPredictive Learning ModelsCA-125 AntigenFibrin Fibrinogen Degradation Productsfibrin fragment DEndometrial malignancyMachine learningNomogramPostmenopausal asymptomatic womenShapley Additive exPlanations (SHAP)

Identifiers

PMID41589874
PMCPMC13105743

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.