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Updated: Jan 20, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk and prognostic nomograms for hepatocellular carcinoma with newly-diagnosed pulmonary metastasis using SEER data
Guanzhi Ye1, Lin Wang1, Zhengyang Hu1
1Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Purpose:
This research aimed to identify risk factors of pulmonary metastasis (PM) from hepatocellular carcinoma (HCC) and prognostic factors of patients with PM from HCC at initial diagnosis.
Methods:
Patients diagnosed with HCC between 2010 and 2015 were reviewed retrospectively in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with PM from HCC at initial diagnosis were identified from the entire cohort. Predictors for PM from HCC were identified by multivariate logistic regression analysis. Independent prognostic factors for patients with PM were determined by univariate and multivariate Cox regression analysis. Nomograms were also constructed for quantifying risk of metastasis and overall survival estimation visually.
Results:
Our research included 30,641 patients diagnosed with HCC, of whom 1,732 cases were with PM from HCC at initial diagnosis. The risk factors causing PM from HCC were age (P = 0.001), race (P < 0.001), primary tumor size (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), alpha-fetoprotein (P < 0.001), bone metastasis (P < 0.001), brain metastasis (P < 0.001), and intrahepatic metastasis (P < 0.001). The significantly prognostic factors for overall survival were age (P = 0.014), T stage (P = 0.009), surgical approach (P < 0.001), and chemotherapy (P < 0.001). Harrell's C-index statistics of two nomograms were 0.768 and 0.687 respectively, indicating satisfactory predictive power.
Conclusions:
This research provided evaluation of risk factors and prognosis for patients with PM from HCC. Two nomograms we developed can be convenient individualized tools to facilitate clinical decision-making.
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