338P Leveraging NSCLC foundation models to predict biomarker testing pattern

ConcertAI’s Patient360™ provided manually curated EHR data from 70,622 NSCLC pts to develop and validate the FM. The dataset was divided into training (80%; N=56,492), validation (10%; N=7,062), and test (10%; N=7,063) sets. The FM employed hierarchical attention to capture inter- and intra-variable across timeframes. Self-supervised learning masked ≈10% of tokens/iteration, and training ran for 350 epochs. Patient embeddings were generated from up to 400 tokens/sequence encompassing 85 unique tokens from 35 clinical variables. Biomarker data was withheld during training and evaluation. A four-layer MLP adaptor predicted testing patterns from embeddings, and performance metrics were compared with literature-reported testing prevalence.

The FM learned embeddings from ≈22M tokens (56,492pts × 400 tokens/pt). AUCs ranged from 0.91-0.97 for ALK, BRAF, EGFR, ROS1, MET, and RET, but were 0.86 for PD-L1 testing. This reflects real-world workflows, where PD-L1 requires separate IHC testing while other biomarkers are assessed via multigene panels. The FM's PPV was 92% for PD-L1 testing (79% literature prevalence) and 97%-99% for gene-panel biomarkers (85%-88% prevalence). Compared to literature-reported untested/undocumented rates of 10–20%, FM identified 34–70% of cases—a 3–5x increase over random selection.

This FM effectively predicts testing patterns for common biomarkers. High PPV and NPV suggest reliable identification of tested and untested/undocumented cases. Performance differences between panel biomarkers and PD-L1 reflect workflow nuances, supporting the model’s clinical relevance.