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
Abstract
A primary goal of current clinical cancer research is the identification of prognostic tumor subtypes. It is increasingly dear 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. We have also shown that information about tumor mass together with patterns of other prognostic factors is related to survival.
We now use a different patient cohort to validate this prognostic approach. A main finding is our identification of a common time of total disease duration (MD) for every HCC patient.
Clinical prognosis at the time of baseline patient evaluation is then calculable as the difference between TDD and the time from disease onset to diagnosis (T-onset). We show that the total pattern of all parameter values and the differences in the relationships between this pattern and a reference pattern that, together with the tumor mass, best reflects the patient's prognosis at baseline.
Our approach led us to identify 15 different composite HCC subtypes. Our results highlight the nearly identical TDD in all patients, which must therefore be a characteristic of the HCC disease, as opposed to the variable quantity of T-onset, which is impacted by multiple macro- and micro-environmental factors.