Evidence map›Paper›PMID 41878711›Full record

ArticleInternational journal of women's health2026

A Preoperative Nomogram Integrating Clinical and Ultrasonic Features to Predict Extra-Pelvic Metastasis in Ovarian Cancer: A Multicenter Retrospective Study.

Yanli Wang, Weihong Lin, Yifang He, Dandan Wang, Xiuming Wu, Shaozheng He, Min Gong, Luhong Li, Guorong Lyu

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Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yanli Wang *Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Weihong Lin *Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Yifang HeDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Dandan WangDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Xiuming WuDepartment of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, People's Republic of China.
Shaozheng HeDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Min GongDepartment of Ultrasound, Chengdu Third People's Hospital, Chengdu, People's Republic of China.
Luhong LiDepartment of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Guorong LyuDepartment of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.ORCID 0000-0003-3123-1138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Ovarian cancer (OC) is a prevalent gynecological malignancy, often diagnosed at an advanced stage with extra-pelvic metastases. The accurate identification of advanced OC is essential for guiding appropriate treatment plans and influencing outcomes. The purpose of this research was to establish a preoperative nomogram integrating clinical and ultrasonic features to predict extra-pelvic metastasis, which may facilitate precise diagnosis and personalized treatment for OC patients. Patients and Methods: This retrospective study included 347 women with OC from three medical centers who had clear pathology and ultrasonic examination before surgery from March 2016 to July 2025. They were divided into two groups according to whether extra-pelvic metastasis occurred: group A without extra-pelvic metastasis (n=164) and group B with extra-pelvic metastasis (n=183). The total patient population was randomly split between a training set (70%) and a validation set (30%). Predictors were selected using LASSO, followed by univariate and multivariate logistic regression analyses. A predictive nomogram was established to predict the risk of extra-pelvic metastasis of OC. Results: Four independent risk factors ascites (OR 7.07, 95% CI 3.54-14.11, p<0.001), maximum tumor diameter (OR 1.08, 95% CI 1.01-1.15, p=0.029), ill-defined boundary (OR 4.20, 95% CI 2.18-8.12, p<0.001), and blood flow score level 4 (OR 4.69, 95% CI 1.70-12.97, p=0.003) were screened using LASSO and logistic regression, and a nomogram was established. The model demonstrated high discriminatory power, with an AUC of 0.860 (95% CI: 0.812-0.908) in the training set and 0.865 (95% CI: 0.798-0.932) in the validation set. The calibration curve and decision curve analysis curve show great performance. Conclusion: The developed nomogram, incorporating readily available clinical and ultrasonic features, provides a valuable tool for individualized prediction of extrapelvic metastasis in OC patients. It serves as a practical tool for preoperative risk stratification to guide clinical decision-making.

Indexed as

extra-pelvic metastasisnomogramovarian cancerultrasound

Identifiers

PMID41878711
PMCPMC13007969

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