Development and validation of a nomogram for predicting outcomes in ovarian cancer patients with liver metastases.

Abstract

Purpose: To develop and validate a nomogram for predicting the overall survival (OS) of ovarian cancer patients with liver metastases (OCLM).

Methods: This study identified 821 patients in the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 574) and a validation cohort (n = 247). Clinical factors associated with OS were assessed using univariate and multivariate Cox regression analyses, and backward stepwise regression was applied using the Akaike information criterion (AIC) to select the optimal predictor variables. The nomogram for predicting the OS of the OCLM patients was constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curves analysis (DCA) in both the training and validation cohorts.

Results: We identified factors that predict OS for OCLM patients and constructed a nomogram based on the data. The ROC, C-index, and calibration analyses indicated that the nomogram performed well over the 1, 2, and 3-year OS in both the training and validation cohorts. Additionally, in contrast to the External model from multiple perspectives, our model shows higher stability and accuracy in predictive power. DCA curves, NRI, and IDI index demonstrated that the nomogram was clinically valuable and superior to the External model.

Conclusion: We established and validated a nomogram to predict 1,2- and 3-year OS of OCLM patients, and our results may also be helpful in clinical decision-making.