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Individual surveillance by competing risk model for patients with hepatocellular carcinoma occurrence in all-cause cirrhosis.
- Source :
- Journal of Cancer Research & Clinical Oncology; Nov2023, Vol. 149 Issue 14, p13403-13416, 14p
- Publication Year :
- 2023
-
Abstract
- Purpose: It was of great significance to identify someone with a high risk of hepatocellular carcinoma (HCC) occurrence and make a diagnosis as early as possible. Therefore, we aimed to develop and validate a new, objective, and accurate prediction model, and convert it into a nomogram to make a personalized prediction of cancer occurrence in cirrhotic patients with different etiologies. Methods: The present study included 938 patients with cirrhosis from January 1, 2011, to December 31, 2012. Patients were prospectively followed-up until January 1, 2018. We used a competing risk model and the Fine–Gray test to develop and validate the prediction model and to plot a nomogram based on the model established. Results: At the end of follow-up, 202 (21.5%) patients developed HCC, with a 5-year incidence of 19.0% (corrected for competing risk model). Based on the competing risk regression method, we built a prediction model including age, gender, etiology, lymphocyte, and A/G ratio. Three groups with different risks were generated on account of tertiles of the 5-year risk predicted by the model. The cumulative 1-, 3-, and 5-year incidences of HCC were 2.0%, 20.8%, and 42.3% in high-risk group, 0.9%, 10.1%, and 17.7% in medium-risk group, and 0%, 2.0%, 8.5% in low-risk group (P < 0.001). The model showed excellent discrimination and calibration in predicting the risk of HCC occurrence in patients with all-cause cirrhosis. Conclusion: The model could make an individual prediction of cancer occurrence and stratify patients based on predicted risk, regardless of the causes of cirrhosis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01715216
- Volume :
- 149
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Journal of Cancer Research & Clinical Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 173106554
- Full Text :
- https://doi.org/10.1007/s00432-023-04911-y