I-CLIP: improved stratification of advanced hepatocellular carcinoma patients by integrating plasma IGF-1 into CLIP score.
OBJECTIVE: Improving the prognostic stratification of unresectable hepatocellular carcinoma (HCC) patients is critically needed. Since patients' survival is closely linked to the severity of the underlying liver disease, and insulin-like growth factor-1 (IGF-1) is produced predominantly in the liver, we hypothesized that IGF-1 may correlate with patients' survival and hence improve the prognostic ability of the Cancer of the Liver Italian Program (CLIP) score. METHODS: Baseline plasma IGF-1 and clinicopathologic parameters were available from 288 patients. Multivariate Cox regression models, Kaplan-Meier curves, and the log-rank test were applied. Recursive partitioning was used to determine the optimal cut point for IGF-1 using training/validation samples. Prognostic ability of the I-CLIP (I = IGF) was compared to CLIP using C-index. RESULTS: IGF-1 significantly correlated with the clinicopathologic features. With an optimal IGF-1 cut point of 26 ng/ml, the overall survival of patients with IGF-1 >26 was 17.7 months (95% CI 13.6-22.8), and with IGF-1 ≤26 was 5.8 months (95% CI 4.0-12.5), p < 0.0001. The concordance probabilities for CLIP and I-CLIP were 0.7037 and 0.7096, respectively (p < 0.0001). CONCLUSIONS: Our preliminary results indicate that I-CLIP significantly improved prognostic stratification of patients with advanced HCC. However, independent validation of our study is warranted.
Duke Scholars
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Related Subject Headings
- alpha-Fetoproteins
- Risk Factors
- Risk Assessment
- Proportional Hazards Models
- Prognosis
- Predictive Value of Tests
- Oncology & Carcinogenesis
- Neoplasm Staging
- Neoplasm Invasiveness
- Multivariate Analysis
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- alpha-Fetoproteins
- Risk Factors
- Risk Assessment
- Proportional Hazards Models
- Prognosis
- Predictive Value of Tests
- Oncology & Carcinogenesis
- Neoplasm Staging
- Neoplasm Invasiveness
- Multivariate Analysis