MRDsteer: quality-aware AI-driven closed-loop optimization enhances ctDNA-based minimal residual disease detection

Accurate identification of ultra-low-frequency tumor-derived variants is critical for circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) profiling. However, current ctDNA analysis workflows largely operate as predefined, sequential pipelines without explicit mechanisms for continuous monitoring of variant-calling performance or adaptive control, thereby limiting detection stability in genomically heterogeneous regions. To address this limitation, we developed MRDsteer, an autonomous closed-loop agent driven by artificial intelligence (AI). MRDsteer monitors the analytical reliability of variant calling during the analysis process using multidimensional quality metrics, such as filtration ratio and strand bias. When the estimated reliability falls below an actionable threshold, MRDsteer triggers localized re-calling only in high-risk genomic regions, instead of repeating the entire analysis. In this way, MRDsteer provides closed-loop control by continuously assessing variant-calling reliability and applying targeted intervention when needed. Comparative analyses in simulated and real-world datasets showed that MRDsteer improved the stability and sensitivity of ctDNA variant detection. Under challenging conditions, including ultra-low variant allele frequencies, MRDsteer demonstrated improved detection performance compared with representative baseline methods. In clinical cohorts, MRDsteer improved ctDNA-based MRD stratification and strengthened progression-free survival separation in the K438 cohort, including both non-small cell lung cancer (NSCLC) and nasopharyngeal carcinoma (NPC) subgroups. These results suggest that MRDsteer may provide a robust and clinically useful computational strategy for sensitive MRD detection and longitudinal ctDNA monitoring.