Prediction model establishment of prognosis factors for distant metastasis of hepatocellular carcinoma based on the SEER database

Background: Distant metastasis in hepatocellular carcinoma (HCC) is an important indicator of poor patient prognosis. Identifying patients who are at high risk of metastasis early on is essential for creating personalized treatment plans, yet currently, there is a scarcity of effective predictive tools. Objective: To investigate the effects of different factors on distant metastasis in HCC patients and to establish a clinical prediction model for predicting distant metastasis in HCC patients. Methods: Our study retrospectively examined 22,318 patients diagnosed with confirmed HCC from the SEER database. Prognostic factors for developing distant metastases in HCC patients were identified by univariate and multivariate logistic regression analyses. Utilizing data from a multivariate logistic regression analysis, we created a nomogram. Its predictive precision was evaluated by analyzing the calibration curve, the area under the curve (AUC) of the receiver operating characteristic curve, decision curve assessment (DCA), and KaplanMeier (KM) curve analysis of overall survival. Finally,the nomogram was visualized with an online calculator. Results: We identified six independent prognostic factors: ethnicity, marital status, tumor size, survival time, surgery, and radiotherapy. The nomogram constructed from these six factors showed good calibration, discrimination, and clinical application value after calibration curve analysis, receiver operating characteristic curve analysis and DCA curve analysis. Besides, Kaplan-Meier survival curves also demonstrated that this nomogram had predictive accuracy. Conclusion: In this research, a nomogram model was created to accurately predict distant metastasis risk in patients with HCC. This study provides guidance for optimizing individual therapies and making better clinical decisions.