A New Approach to Analysis of Clinical Data and Prognostication for Patients with Hepatocellular Carcinoma, Based Upon a Network Phenotyping Strategy (NPS) Computational Method.

OBJECTIVES

There is a multi-component nature of the influences on HCC progression but integrating them has been difficult. Network phenotyping strategy (NPS) integrates all multi-component relationship facets of HCC progression and aims to lead to a new way of understanding human HCC biology.

METHODS

We converted baseline patient demographics, tumor characteristics, blood hematology and liver function test results, consisting of values of 17 standard clinical variables, collected time-coherently at the index visit, into a graph-theoretical data representation.

RESULTS

These data were analyzed by NPS, which processes the patient parameter values together with their complete relationships network. NPS identified 25 disease-progression ordered HCC phenotypes. Clinically relevant NPS results are a) Portal vein thrombosis incidence during HCC progression stratified into 5 narrow ranges; b) NPS identified patients according to aggressive, slow and intermediate tumor growth sub-types; c) Personalized prognostication of mortality was achieved by the 25 NPS phenotypes, independently optimized for respective phenotype sub-cohorts.

CONCLUSION

The NPS results were implemented as an internet application (https://apkatos.github.io/webpage_nps), where input of 17 clinical parameters provides the patient phenotype, phenotype-characteristic average mortality and personal survival estimate.