Journal of investigative surgery : the official journal of the Academy of Surgical Research · 2025
A Novel Nomogram for Prediction of Survival in Patients with Small Isolated Hepatocellular Carcinoma: A Population-Based and Externally Validated Study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Li Ziqiang, Hong Qingyong, Ni Zhizhan, Guo Zhidong, Shi Qimeng, Wang Xianqing, Huang Qi, Li Kun, Ge Bujun
Аннотация
BACKGROUND: Current prognostic tools lack precision for small hepatocellular carcinoma (HCC) (≤5 cm) and fail to capture tumor heterogeneity. This study aimed to construct a nomogram to predict survival in patients with isolated small HCC.
METHODS: A total of 5187 eligible patients from the SEER database were randomized into training and internal validation cohorts, while 180 patients from Zhongnan Hospital of Wuhan University served as an external validation cohort. Cox regression analysis identified factors affecting cancer-specific survival (CSS), which were used to construct the nomogram. Performance was evaluated using the consistency index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, we used Kaplan-Meier curves for survival analysis.
RESULTS: We identified eleven independent risk factors influencing CSS in isolated small HCC patients. In the training, internal validation, and external validation cohort, the C-index of the nomogram was 0.702, 0.717, and 0.729, respectively. AUC, calibration curves, and DCA curves showed good predictive accuracy and clinical utility. Kaplan-Meier curves revealed significant CSS differences between high- and low-risk groups. Additionally, we developed an online prediction tool.
CONCLUSIONS: The nomogram effectively predicts CSS in isolated small HCC patients and may aid in individualized clinical decision-making.
METHODS: A total of 5187 eligible patients from the SEER database were randomized into training and internal validation cohorts, while 180 patients from Zhongnan Hospital of Wuhan University served as an external validation cohort. Cox regression analysis identified factors affecting cancer-specific survival (CSS), which were used to construct the nomogram. Performance was evaluated using the consistency index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, we used Kaplan-Meier curves for survival analysis.
RESULTS: We identified eleven independent risk factors influencing CSS in isolated small HCC patients. In the training, internal validation, and external validation cohort, the C-index of the nomogram was 0.702, 0.717, and 0.729, respectively. AUC, calibration curves, and DCA curves showed good predictive accuracy and clinical utility. Kaplan-Meier curves revealed significant CSS differences between high- and low-risk groups. Additionally, we developed an online prediction tool.
CONCLUSIONS: The nomogram effectively predicts CSS in isolated small HCC patients and may aid in individualized clinical decision-making.