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Updated: Nov 13, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Survival analysis of second primary malignancies after cervical cancer using a competing risk model: implications for
Runmei Li1, Yue Zhang1, Bingqing Ma1
1Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
Background:
Previous studies have reported an increased risk for second primary malignancies (SPMs) after cervical cancer (CC). This study aims to quantify and assess the risk of developing SPMs in long-term survivors of CC.
Methods:
A population-based cohort of CC patients aged 20-79 years was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk model and corresponding nomogram were constructed to predict the 3-, 5-, and 10-year cumulative risks of SPMs. A Fine-Gray plot was created to validate the model. Finally, we performed decision curve analysis (DCA) to evaluate the clinical usefulness of the model by calculating the net benefit.
Results:
A total of 34,295 patients were identified, and approximately 6.3% of the study participants developed SPMs. According to the multivariable competing-risk model, older black CC survivors with localized disease who were treated with radiation therapy were more susceptible to SPMs. The 3-, 5-, and 10-year cumulative incidences of SPMs were 2.5%, 3.6%, and 6.2%, respectively. Calibration curves showed good agreement between the predicted and observed models. The DCA yielded a wide range of risk thresholds at which the net benefits could be obtained from our proposed model.
Conclusions:
This study provides physicians with a practical, individualized prognostic estimate to assess the risk of SPMs among CC survivors. CC survivors remain at a high risk of developing SPMs, and further surveillance should focus especially on the patients with black race, older age, localized disease, or those having received radiation therapy.
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