Abstract
To evaluate the performance of clustering methods used in the prognostic assessment of categorical clinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognostic nomogram for clinical decisions. A total of 332 newly diagnosed HCC patients treated with hepatic resection during 2006-2009 were enrolled. Patients were regularly followed up at outpatient clinics. Clustering methods including the Average linkage, k-modes, fuzzy k-modes, PAM, CLARA, protocluster, and ROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI). Then the clustering variable, age group, tumor size, number of tumor and vascular invasion were studied in a multivariable Cox regression model. A prognostic nomogram was constructed for clinical decisions. The ROCK was best in both the overlapping and non-overlapping cases performed to assess the prognostic value of platelet-based indices. Patients with categorical platelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCC recurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0.01). Tumor size, number of tumor and blood vessel invasion were also associated with high risk of HCC recurrence (all p<0.01). The nomogram well predicted HCC patient survival at 3 and 5 years. A cluster of platelet-based indices combined with other clinical covariates could be used for prognosis evaluation in HCC.
Highlights
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and the burden of this devastating cancer is expected to increase further in coming years (Nguyen et al, 2009; Venook et al, 2010)
Clustering methods including the Average linkage, k-modes, fuzzy k-modes, Partitioning Around Medoids (PAM), CLustering LARge Applications (CLARA), protocluster, and RObust Clustering using linKs (ROCK) were compared by Monte Carlo simulation, and the optimal method was applied to investigate the clustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartate aminotransferase activity/platelet count ratio index (APRI)
Many factors such as tumor size, number of tumor, vascular invasion and resection margin status are associated with the prognosis of hepatocellular carcinoma (HCC) resection, it is necessary to find a potential prognostic cluster that is available before surgery, because it can be used to predict and assess the prognostic status for HCC patients who received tumor resection
Summary
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and the burden of this devastating cancer is expected to increase further in coming years (Nguyen et al, 2009; Venook et al, 2010). In Asian region, the incidence of HCC exceeds 30 cases per 100, 000 residents annually, which is due to the high prevalence of chronic viral hepatitis, mainly chronic hepatitis B (Teo et al, 2002; Gao et al, 2012; Guo et al, 2012). Many factors such as tumor size, number of tumor, vascular invasion and resection margin status are associated with the prognosis of HCC resection, it is necessary to find a potential prognostic cluster that is available before surgery, because it can be used to predict and assess the prognostic status for HCC patients who received tumor resection.
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