A personalized prognostic model for long-term survival in patients with intrahepatic cholangiocarcinoma: a retrospective cohort study
10.4174/astr.2024.107.1.16
- Author:
Xianhui DONG
1
;
Pengwei ZHANG
;
Chunhong YE
;
Li LI
Author Information
1. Center for Pre-Disease Treatment and Health Management, Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, China
- Publication Type:ORIGINAL ARTICLE
- From:Annals of Surgical Treatment and Research
2024;107(1):16-26
- CountryRepublic of Korea
- Language:EN
-
Abstract:
Purpose:This study aimed to determine the optimal cutoff points for age and tumor size of patients with intrahepatic cholangiocarcinoma (ICC) and to establish and verify a predictive nomogram of overall survival at 1, 3, and 5 years.
Methods:From the SEER (Surveillance, Epidemiology, and End Results) database, 1,325 ICC patients were selected and randomly divided into training and testing cohorts at a 7:3 ratio. Using the X-tile software, age and tumor size were classified into 3 subgroups: ≤61, 62–74, and ≥75 years and ≤35, 36–55, and ≥56 mm. Subsequently, univariate and multivariate Cox regression analyses were performed using the R software in the training cohort to determine independent risk factors, compile the prediction nomogram, and verify it with the testing cohort findings.
Results:The C-indexes of the new prediction nomograms in the training and testing cohorts were 0.738 (95% confidence interval [CI], 0.718–0.758) and 0.750 (95% CI, 0.72–0.78), respectively. Furthermore, the areas under the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves based on the nomogram were 0.792, 0.853, and 0.838, respectively, higher than the ROC based on the 7th and 8th editions of the American Joint Cancer Commission (AJCC) staging system.
Conclusion:This study established and verified a prognostic nomogram that improved the accuracy of the 1-, 3-, and 5-year survival predictions for ICC patients, compared with that based on the 7th and 8th editions of the AJCC staging system, and can help clinicians make personalized survival predictions.
