Validation of the Concept of a Common Typical Time of Disease Duration for Hepatocellular Carcinoma Patients Using the Fisher Information Processing of Tumor Imaging Results Combined With Network Phenotyping Strategy Quantification of Individual Patient Clinical Profile Patterns.

A primary goal of current clinical cancer research is the identification of prognostic tumor subtypes. It is increasingly clear that tumor growth depends on both internal tumor factors, and factors that are external to the tumor, such as microenvironment. We recently showed that parameter values alone are less important than the patterns of all patient parameters together for the identification of prognostic subtypes and have identified a network phenotyping strategy method to quantitatively describe the dependency of the tumor on the environment, to characterize hepatocellular carcinoma (HCC) subtypes.

Evaluation of total hepatocellular cancer lifespan, including both clinically evident and preclinical development, using combined network phenotyping strategy and fisher information analysis.

We previously showed that for hepatocellular cancer (HCC) prognostication, disease parameters need to be considered within a total personal clinical context. This requires preserving the coherence of data values, observed simultaneously for each patient during baseline diagnostic evaluation. Application of the Network Phenotyping Strategy (NPS) provided quantitative descriptors of these patient coherences.