Development of prediction model to estimate future risk of ovarian lesions: A multi-center retrospective study

Bilin Jing, Gaowen Chen, Miner Yang, Zhi Zhang, Yue Zhang, Jingyao Zhang, Juncheng Xie, Wenjie Hou, Yong Xie, Yi Huang, Lijie Zhao, Zhao Li-jie, Hua Yuan, Weilin Liao, Yifeng Wang
Preventive medicine reports · 2023 · vol. 35 , pp. 102296 · doi:10.1016/j.pmedr.2023.102296 · PMID:37455762 · W4381930338
article OA: gold CC0 ⤵ 3 in-corpus citations
🔓 Open OA copy View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-09 ⓘ

This study developed and validated prediction models, particularly the Random Forest model, using serological biomarkers to accurately estimate the preoperative risk of various ovarian lesions, including benign, malignant, borderline tumors, serous adenocarcinoma, and endometriosis.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

Abstract

Background: To develop the preoperative prediction of ovarian lesions using regression-based statistics analyses and machine learning methods based on multiple serological biomarkers in China. Methods: 1137 patients with ovarian lesions in Zhujiang Hospital and 518 patients in others hospital in China were randomly assigned to training, test and external validation cohorts. Five machine learning classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Classifier (SVC), K-nearest Neighbor (KN), Multi-Layer Perceptron (MLP) and the Lasso-Logistics prediction model (LLRM) were used to derive diagnostic information from 23 predictors. Results: The RF model had a high diagnostic value (AUC = 0.968) in predicting benign and malignant ovarian disease. Age and MLR were also potential diagnostic indicators for predicting ovarian disease except tumor indicators. The RF model well distinguished borderline ovarian tumors (AUC = 0.742). The RFM had a high predictive power to identify ovarian serous adenocarcinoma (AUC = 0.943) and ovarian endometriosis cysts (AUC = 0.914). Conclusions: The RF models can effectively predict adnexal lesions, promising to be adjuncts to the preoperative prediction of ovarian cancer.

My notes (saved in your browser only)

Condition tags

endometriosis

Citation neighborhood (sparse)

Too few in-corpus citations on either side for a chart; here are the lists.

Cites (1)

Cited by (3)

References (21)

Cited by (3)

Source provenance

europepmc
last seen: 2026-06-04T01:30:01.192114+00:00
openalex
last seen: 2026-06-04T00:00:01.174412+00:00
pubmed
last seen: 2026-05-30T00:33:49.040417+00:00
License: CC0 · commercial use OK