Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study

Abstract

Abstract Purpose: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients. Methods: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan–Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P<0.05) to determine the independent prognostic factors of synchronous lung metastases. Results: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases. Conclusion: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.