Deep cytomorphology identifies erythroid skewing and monocytic morphology to predict TKI sensitivity in CML patients

The cellular composition of the chronic myeloid leukemia (CML) bone marrow (BM) beyond granulocyte enrichment remains poorly understood. We analyzed 1548 routinely stained BM aspirate slides from 598 patients across seven sites using deep learning-based image analysis to identify cytomorphological markers predictive of major molecular response. Erythroid precursor enrichment, monocyte nuclear lobulation, and low peripheral leukocyte count were associated with improved tyrosine kinase inhibitor (TKI) response. These features were validated both visually and computationally in two independent cohorts. We developed a Morphoclinical model integrating these image-derived and clinical variables, outperforming (area under the receiver-operating curve [AUROC] 0.76) the clinically used EUTOS long-term survival score (AUROC 0.53) and BCR::ABL1 halving time (AUROC 0.61). Notably, poor-risk patients treated with second-generation TKIs achieved outcomes similar to favorable-risk patients on imatinib. These results underline the overlooked prognostic value of BM cytomorphology to refine risk stratification and support more personalized frontline therapy in CML.