AFP as an HCC surveillance tool detects a significant number of treatable HCC in patients with satisfactory outcomes.

<p><b>A</b>) Survival for total HCC cohort diagnosed with HCC between 1/1/2009 and 31/12/2014. <b>B</b>) The role of AFP in HCC detection. Method of HCC detection for the 133 patients within HCC surveillance programme at the time of diagnosis, chequered area within AFP pickup group represents the 28/49 patients in whom a recent US had not been performed—see text for details. <b>C</b>) Individual AFP levels at time of diagnosis for patients diagnosed with HCC, AFP values plotted at log10; AFP = 6 (local ULN; yellow) and AFP = 20 (red) are shown. All columns p<0.0001 to one another by Kruskal Wallis test with Dunns multiple comparison. <b>D</b>) Survival of patients with HCC diagnosed through surveillance screening either through US or AFP mediated conversion to CT/MRI imaging, error bars = SEM, p value denotes Mantel Cox. <b>E</b>) Therapy offered to patients within each group (US detected n = 61 and AFP detected n = 49) of patients with HCC detected during surveillance; all p>0.05 by 2 way Anova. Of the 11 and 9 patients listed for liver transplantation, 2 (due to tumour growth) and 1 (due to frailty) were delisted from the waiting list whilst awaiting transplantation in US and AFP detected groups respectively.</p

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.</p

Exploratory analysis of HCC surveillance cohort.

<p>Exploratory analysis of HCC surveillance cohort.</p

Model coefficients obtained from full-data and windowed analyses in static mode (wave 48) and dynamic mode (wave 1).

<p>Model coefficients obtained from full-data and windowed analyses in static mode (wave 48) and dynamic mode (wave 1).</p

Case-controls study assessing the performance of our dynamic Bayesian algorithm for identifying HCV cases at higher risk of HCC-diagnosis during 2009–2014.

<p>Case-controls study assessing the performance of our dynamic Bayesian algorithm for identifying HCV cases at higher risk of HCC-diagnosis during 2009–2014.</p

Demographics and route to diagnosis in HCC patients.

<p>Demographics and route to diagnosis in HCC patients.</p

Dynamic AFP changes associated with HCC development.

<p><b>A</b>) Plotting each individual’s time course of serum AFP relative to diagnosis an elevation in values is observed prior to diagnosis. Here all individuals in the HCC review in whom AFP influenced clinical management (n = 49) are charted with AFP on a log₁₀ scale. <b>B</b>) Graphic demonstrates the concept of gradient and intercept over a specific individual’s time course. <b>C</b>) The screening cohort of 1509 patients followed over time for development of HCC, overall incidence at end of index screening (1186 days from 01/01/2009, total HCC free % = 93.7).</p