A Novel Prognostic Nomogram for Patients With Recurrence of Intrahepatic Cholangiocarcinoma After Initial Surgery
- PMID: 32300559
- PMCID: PMC7142225
- DOI: 10.3389/fonc.2020.00434
A Novel Prognostic Nomogram for Patients With Recurrence of Intrahepatic Cholangiocarcinoma After Initial Surgery
Abstract
Background: The prognosis of patients with post-operative recurrent intrahepatic cholangiocarcinoma (ICC) is at great variance. We aimed to propose a novel efficient prognostic nomogram in facilitating the risk stratification for post-operative recurrent ICC patients. Methods: From 2000 to 2016, a total of 237 post-operative recurrent ICC patients were enrolled in this study, and randomly divided into training (n = 178) and validation cohorts (n = 59) at a ratio of 3:1. The performance of this nomogram was assessed by discrimination, calibration, and clinical usefulness, and the results were compared with four other currently used ICC staging systems. Results: On multivariate analysis of the training cohort, serum CA 19-9, albumin-bilirubin grade at recurrence, time from primary resection to recurrence, tumor number at recurrence, and treatment for recurrence were selected for the model. The concordance index (C-index) of our model was 0.791 [95% confidence interval (CI), 0.736-0.846], which was statistically higher than the values of the following systems: American Joint Committee on Cancer (AJCC) 8th edition (0.610), Liver Cancer Study Group of Japan (0.613), Nathan (0.582), and Okabayashi (0.600; P < 0.001 for all). The nomogram performed well in terms of calibration when compared with actual observation. The findings were supported by the validation cohort. Conclusions: Compared with four currently used staging systems for ICC, our nomogram showed more promising clinical utility in improving individualized predictions of survival for post-operative recurrent ICC patients.
Keywords: intrahepatic cholangiocarcinoma; nomogram; post-recurrence survival; recurrence; resection.
Copyright © 2020 Xing, Lu, Huang, He, Song, Guo and Li.
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