Alpha-Fetoprotein Detection of Hepatocellular Carcinoma Leads to a Standardized Analysis of Dynamic AFP to Improve Screening Based Detection
Fig 3
Using dynamic analysis of AFP provides a methodology for identifying patients at high risk of HCC.
A) Workflow for the development of an algorithm for HCC detection using AFP. The HCC surveillance cohort refined to patients with specific characteristics prior to formal Bayesian analysis in static and dynamic modes. In static mode a trigger zone was established, which was then tested dynamically. Estimated patient-specific intercept and gradient parameters plotted against each other. Estimates were taken from `windowed' version B) `full-data' version C) of full-trajectory retrospective Bayesian analysis. Triangles denote confirmed early-diagnosed HCC cases. Diagonal lines define regions of parameter space (above the line) that might indicate emerging HCC cases: purple—passes through (x, y) = (-0.01, 1) and (0, log20); brown—passes through (x, y) = (-0.01, 0.5) and (0, 1); yellow—passes through (x, y) = (-0.01, 0.5) and (0, log20). The area to the above/left of the yellow line was used to represent the area of ‘high risk’ characteristics of AFP. D) Illustration of triggering across waves of prospective Bayesian analysis. All HCC patients from the HCV group are shown, along with an equal number of non-HCC cases from the same group. A point is plotted for each trigger (HCCs denoted by triangles and non-HCCs by circles); a horizontal line is shown for patients who did not trigger at all. Points of a lighter shade are used to indicate that the patient-specific data are the same as in the preceding wave due to that patient's data set having ceased to accrue more AFPs in the training data-set.