Adaptive Risk Models for Early Cancer Detection: Why Diagnostics Should Keep Learning

Most blood-based cancer detection tests are locked at launch, even though the populations, assays and clinical practices around them keep changing. We argue that multi-analyte early-detection models should be adaptive: recalibrated as outcome-linked results accumulate, and governed by versioning, human-gated updates, drift monitoring and documented re-validation. Using pancreatic ductal adenocarcinoma as a case study, we describe how the CLIA framework and FDA Predetermined Change Control Plans can support safe, continuous improvement.