A leakage-aware entropy screening protocol for structured biomarker panel evaluation in ovarian cancer risk modelling

In biomedical risk modeling, entropy-based feature screening is being increasingly used, but covariance statistics have been commonly calculated based on entire datasets before validation. It may introduce a small amount of information leakage and spur a perceived panel synergy especially when administrative or post-outcome variables are present. This article presents a leakage-aware entropy screening protocol for structured biomarker panel evaluation. The workflow integrates (i) systematic administrative variable auditing, (ii) fold-restricted von Neumann entropy estimation derived from covariance-based density matrices, and (iii) cross-validated predictive benchmarking within a unified implementation framework. Entropy computation is strictly confined to training partitions under stratified cross-validation, and panel definitions are fixed prior to analysis to avoid grouping bias. The protocol is demonstrated using a structured ovarian cancer dataset from the PLCO Cancer Screening Trial to illustrate leakage-controlled panel-level entropy estimation and validation-consistent benchmarking. The workflow provides an auditable and reproducible framework for multivariate panel screening and is adaptable to other structured biomedical datasets requiring grouped feature evaluation.