Prospective validation of AOC threshold-to-efficacy mapping

Determine whether specified Algorithm-to-Outcome Concordance (AOC) threshold ranges—such as AOC greater than 0.65 predicting hazard ratio below 0.65—reliably correspond to improved clinical efficacy in prospective melanoma neoantigen vaccine trials, confirming the predictive validity of simulation-derived cutoffs.

Background

The authors propose AOC as a quantitative metric integrating algorithm discrimination, clinical calibration, and inter-trial heterogeneity. They present simulation-derived threshold ranges that suggest how AOC values may correspond to clinical efficacy (e.g., lower hazard ratios).

However, these mappings are currently provisional and unconfirmed by prospective trials. Establishing the validity of these thresholds is necessary to operationalize AOC in trial design, algorithm selection, and regulatory decision-making.

References

For thresholds: Simulations tied to real HR from 6 trials yield provisional ranges (e.g., AOC>0.65 linked to HR<0.65 in 70% cases), but these require prospective validation—no current trials confirm.

— A Proposed Framework for Quantifying AI-to-Clinical Translation: The Algorithm-to-Outcome Concordance (AOC) Metric  (2510.26685 - Yu et al., 30 Oct 2025) in Theoretical Foundation of AOC, Subsection 2. Range Analysis with Contour Plots