ArticleTranslational cancer research2020
A clinical model to predict the risk of liver metastases in newly diagnosed ovarian cancer: a population-based study.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine Learning Models for Predicting Liver Metastasis at Diagnosis and Overall Survival in Ovarian Cancer: A SEER-Based Study.International journal of women's health · 2026Article
- Impact of single-site distant metastases on prognosis in advanced ovarian cancer: a Surveillance, Epidemiology, and End Results population-based study.Translational cancer research · 2025Article
- Effect of different treatment modalities on ovarian cancer patients with liver metastases: A retrospective cohort study based on SEER.PloS one · 2024Article
- LINC00641 inhibits the proliferation and invasion of ovarian cancer cells by targeting miR-320a.Translational cancer research · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLiver metastases are important in determining the prognosis of ovarian cancer. We aimed to develop and validate nomograms to predict the risk of liver metastases in patients with early-stage ovarian cancer.
methodsA total of 13,487 patients were enrolled in the study based on their records in the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors of liver metastases were assessed based on univariable and multivariable logistic regression. A nomogram was also formulated based on the results of multivariable logistic analysis. The area under the receiver-operating characteristic curve was calculated to evaluate the discrimination abilities of the metastasis-related factors and liver metastases nomogram. A calibration plot was generated to analyze the consistency between the observed probability and predicted probability of liver metastases in patients with ovarian cancer.
resultsFour related factors were determined based on univariable and multivariable logistic regression, including the T1 stage, N1 stage, and presence of lung and bone metastases. The liver metastases nomogram composed of four features could be used to determine the prediction effect. The calibration plot showed good consistency between the nomogram prediction and actual observation. The receiver-operating characteristic curve showed that the forecast nomogram exhibited a good forecast value.
conclusionsThis clinical prediction model has high accuracy to identify patients with newly diagnosed ovarian cancer who carry a high risk of liver metastases and provide a personalized treatment plan for these patients.
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