Deep Learning Enables Automated Detection of Mature B Cell Neoplasms by Flow Cytometry

Multiparameter flow cytometry is essential for diagnosing mature B-cell lymphomas, yet analysis remains largely manual, time-consuming, and subject to inter-operator variability. We developed an automated system for B-cell neoplasm detection using a three-stage deep learning architecture that produces interpretable intermediate outputs. The Light-chain module classifies each cell's surface immunoglobulin expression. The Cell-level module combines original flow parameters with engineered features capturing light-chain neighborhood and spatial position to identify abnormal cells. The Sample-level module renders case-level diagnoses from cells ranked by abnormality score. We evaluated this system on 3,070 clinical specimens from Memorial Sloan Kettering Cancer Center, comprising peripheral blood (n=1,369), bone marrow (n=316), and tissue (n=1,385) samples. The system achieved 97.5% case-level accuracy with 95.6% sensitivity and 98.9% specificity. Predicted abnormal cell counts correlated with expert manual counts (R²= 0.962 [95% CI: 0.937-0.988], slope=0.998 [0.983-1.014], intercept=-0.011 [-0.095 to 0.073]). Ablation experiments demonstrated that engineered features provide complementary information: light-chain neighborhood grounds spatial features that otherwise introduce noise, enabling the full model to outperform any feature subset. The system generates visualization outputs including marker expression comparisons and dimensionality-reduced embeddings colored by abnormality score, allowing pathologists to verify that flagged populations form coherent clusters with phenotypes consistent with disease. Features extracted from abnormal populations also supported lymphoma subtypes prediction (AUROC=0.925). This work demonstrates that automated flow cytometry interpretation can achieve high diagnostic accuracy while providing transparent, verifiable outputs suitable for clinical integration.