Cross-modal fusion of cytomorphology and

Introduction: The bone marrow immune microenvironment (BMME) shapes treatment response in multiple myeloma (MM), yet routine diagnostic workup primarily assesses tumor burden rather than immune competence. We developed ImmunoCast-MM, a cross-modal deep learning framework that extracts immunologically relevant signals from two examinations routinely performed at diagnosis: Wright-Giemsa-stained bone marrow aspirate smears and whole-body Methods: The cytomorphology branch used DinoBloom embeddings to classify individual cells across five hierarchical levels. The PET/CT branch generated a multi-organ inflammation fingerprint from tumor, spleen, lymph node, and diffuse bone marrow compartments. A contrastive fusion module aligned the two imaging modalities with a flow cytometry reference panel and generated an Immune Dysfunction Index (IDI) along a learned effector-suppressor axis. ImmunoCast-MM was evaluated retrospectively in 243 patients with newly diagnosed MM. Associations with flow cytometric measurements, progression-free survival, and daratumumab response were assessed, with adjustment for International Staging System stage, cytogenetic risk, and age. Results: On a held-out validation subset, decoded cytomorphologic indices correlated with matched flow cytometric fractions, with Spearman Discussion: ImmunoCast-MM reframes two standard diagnostic examinations as non-invasive profilers of the BMME. The framework may support risk assessment and immunotherapy stratification, particularly in centers without access to high-dimensional flow cytometry.