Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study

Siqi Cao, Xingzhe Li, Xin Zheng, Jiaxin Zhang, Ziyao Ji, Yanjun Liu
Scientific reports · 2025 · vol. 15(1) , pp. 13621 · doi:10.1038/s41598-025-96093-5 · PMID:40253412 · PMC12009384 · W4409597770
article OA: gold CC0 ⤵ 5 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-10 ⓘ

This study developed and validated a random forest model using six features to predict severe pelvic endometriosis, with the negative sliding sign being the most impactful predictor.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This single-center retrospective study analyzed 308 surgically diagnosed endometriosis patients to identify risk factors for severe disease and develop machine-learning models to predict severe endometriosis using 39 demographic, laboratory, and transvaginal ultrasound variables. Using LASSO for feature selection, seven algorithms (including logistic regression, random forest, and others) were trained with 10-fold cross-validation and tested on a held-out set, with the random forest achieving the best discrimination (AUROC 0.744); reducing to six top features produced an explainable final RF model where the “negative sliding sign” contributed the most to prediction. The paper notes that its performance reflects the retrospective, single-center dataset and that staging relied on rASRM defined by intraoperative visualization and pathology, which may limit generalizability. This paper is centrally about endometriosis — it develops and validates a predictive machine-learning model for severe pelvic endometriosis based on preoperative clinical and ultrasound data.

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Abstract

This study aimed to explore potential risk factors for severe endometriosis and to develop a model to predict the risk of severe endometriosis. A total of 308 patients with endometriosis were analyzed. Least absolute shrinkage and selection operator (LASSO) was performed to identify the potential risk factors for severe endometriosis. Then, we used seven machine learning (ML) algorithms to construct the predictive models. Finally, SHapley Additive exPlanations (SHAP) interpretation was performed to evaluate the contributions of each factor to risk prediction. About 59.2% (183/308) of patients were diagnosed with severe endometriosis. The random forest (RF) model performed best in discriminative ability among the seven ML models, achieving an area under the curve (AUC) of 0.744. After reducing features according to feature importance rank, an explainable final RF model was established with six features. From the SHAP map, we found that the negative sliding sign had the greatest impact on the diagnostic performance of the RF model. This study provided a personalized risk assessment for the development of severe endometriosis, which may enable early identification of high-risk patients, facilitating timely intervention and optimized treatment strategies.

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Outcome instruments

VAS-pain MUSA rASRM

Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (31)

Cited by (5)

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