Prognostic Evaluation of Categorical Platelet-based Indices Using Clustering Methods Based on the Monte Carlo Comparison for Hepatocellular Carcinoma


Objectives: To evaluate the performance of clustering methods used in the prognostic assessment of categoricalclinical data for hepatocellular carcinoma (HCC) patients in China, and establish a predictable prognosticnomogram for clinical decisions. Materials and
Methods: A total of 332 newly diagnosed HCC patients treatedwith 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, andROCK were compared by Monte Carlo simulation, and the optimal method was applied to investigate theclustering pattern of the indices including platelet count, platelet/lymphocyte ratio (PLR) and serum aspartateaminotransferase 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 prognosticnomogram was constructed for clinical decisions.
Results: The ROCK was best in both the overlapping and nonoverlappingcases performed to assess the prognostic value of platelet-based indices. Patients with categoricalplatelet-based indices significantly split across two clusters, and those with high values, had a high risk of HCCrecurrence (hazard ratio [HR] 1.42, 95% CI 1.09-1.86; p<0·01). Tumor size, number of tumor and blood vesselinvasion were also associated with high risk of HCC recurrence (all p<0·01). The nomogram well predicted HCCpatient survival at 3 and 5 years.
Conclusions: A cluster of platelet-based indices combined with other clinicalcovariates could be used for prognosis evaluation in HCC.