Translational cancer research · 2025
Prognostic prediction nomogram of elderly patients with metastasis colon cancer and distant metastasis pattern: a SEER database analysis.
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Luo Xing, Tang Jincheng, Peng Wei, Liu Fang
Аннотация
BACKGROUND: Elderly patients with metastatic colon cancer (CC) face a poor prognosis, and there is a critical lack of individualized survival prediction tools for this population. To address this gap, our study aimed to develop and validate a novel prognostic nomogram that integrates multiple clinical factors to accurately predict overall survival (OS) and guide treatment decisions.
METHODS: Patients aged over 60 years with pathologically confirmed metastatic CC were identified from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016). Univariate and multivariate logistic regression analyses were used to identify factors associated with distant metastasis (bone, brain, liver, lung). Patients were randomly assigned (1:1) into training and validation cohorts. OS was the primary endpoint. Prognostic factors were determined using Cox regression in the training cohort. A nomogram was developed based on independent predictors and validated using time-dependent ROC curves, calibration plots, and decision curve analysis (DCA).
RESULTS: A total of 6,851 elderly patients with metastatic CC were included. The median age was 70 years. Most patients received surgery (78%) and chemotherapy (66%). Twelve independent predictors of OS were identified, including age, tumor site, histologic grade, tumor-node-metastasis (TNM) stage, treatment strategies, carcinoembryonic antigen (CEA) level, and metastasis pattern. The nomogram demonstrated strong discriminatory ability with areas under the curves (AUCs) of 0.785, 0.786, and 0.795 for 1-, 3-, and 5-year OS in the training cohort, and 0.792, 0.784, and 0.793 in the validation cohort. Calibration curves showed good agreement between predicted and actual outcomes. DCA confirmed clinical utility. Patients were effectively stratified into low-, medium-, and high-risk groups based on nomogram scores.
CONCLUSIONS: This internally validated nomogram provides reliable individualized survival predictions for elderly patients. It offers a practical tool to assist clinicians in optimizing treatment strategies and risk stratification for this vulnerable population.
METHODS: Patients aged over 60 years with pathologically confirmed metastatic CC were identified from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016). Univariate and multivariate logistic regression analyses were used to identify factors associated with distant metastasis (bone, brain, liver, lung). Patients were randomly assigned (1:1) into training and validation cohorts. OS was the primary endpoint. Prognostic factors were determined using Cox regression in the training cohort. A nomogram was developed based on independent predictors and validated using time-dependent ROC curves, calibration plots, and decision curve analysis (DCA).
RESULTS: A total of 6,851 elderly patients with metastatic CC were included. The median age was 70 years. Most patients received surgery (78%) and chemotherapy (66%). Twelve independent predictors of OS were identified, including age, tumor site, histologic grade, tumor-node-metastasis (TNM) stage, treatment strategies, carcinoembryonic antigen (CEA) level, and metastasis pattern. The nomogram demonstrated strong discriminatory ability with areas under the curves (AUCs) of 0.785, 0.786, and 0.795 for 1-, 3-, and 5-year OS in the training cohort, and 0.792, 0.784, and 0.793 in the validation cohort. Calibration curves showed good agreement between predicted and actual outcomes. DCA confirmed clinical utility. Patients were effectively stratified into low-, medium-, and high-risk groups based on nomogram scores.
CONCLUSIONS: This internally validated nomogram provides reliable individualized survival predictions for elderly patients. It offers a practical tool to assist clinicians in optimizing treatment strategies and risk stratification for this vulnerable population.