A clinical model to predict the risk of liver metastases in newly diagnosed ovarian cancer: a population-based study.

Background: Liver 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.

Methods: A 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.

Results: Four 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.

Conclusions: This 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.