Quantitative morphological parameters of bone marrow plasma cells are predictive of outcomes in plasma cell dyscrasias

Abstract Introduction: Morphologic evaluation of bone marrow cells remains a cornerstone of the MICM (morphology-immunophenotyping-cytogenetics-molecular biology) diagnostic framework in hematologic malignancies. In plasma cell neoplasms, including multiple myeloma (MM) and plasma cell leukemia (PCL), neoplastic plasma cells exhibit marked morphological heterogeneity. Characteristically, plasma cells are defined by abundant basophilic cytoplasm and eccentrically positioned nuclei, reflecting their role as terminally differentiated, immunoglobulin-secreting cells. However, PCL, as a highly aggressive form of plasma cell neoplasm, often displays distinct morphological features compared to typical MM. To date, the potential association between plasma cell morphology and clinical prognosis has not been systematically investigated. This study explored the potential prognostic value of plasma cell morphology in patients with plasma cell dyscrasias. Methods: This retrospective study included 347 newly diagnosed multiple myeloma (MM) patients and 30 patients with either primary or secondary plasma cell leukemia (PCL). Wright-Giemsa-stained bone marrow smears (BMSs) and corresponding clinical data were collected at diagnosis. All BMSs were scanned under 100× oil immersion using an automated imaging platform, with a minimum of 500 nucleated cells captured per case. Following standardized pipelines for cell detection, segmentation, and quality control, plasma cells from MM and PCL cohorts were automatically identified using a previously trained cell classification model (unpublished). Pixel-level segmentation masks for nuclei and whole-cell boundaries were generated using a U-Net–based deep learning algorithm, enabling quantitative measurement of nucleus-to-plasma ratio and nuclear skewness for each plasma cell. The mean values of these morphologic parameters were calculated per case and analyzed in relation to clinical baseline characteristics for collinearity. Correlations with continuous variables were assessed using Spearman's rank correlation, while non-parametric tests were applied for categorical comparisons. Finally, morphology-related variables and clinical indicators associated with prognosis were evaluated using multivariate Cox regression analysis. Results: The plasma cell classification model achieved a high accuracy of 0.9747 with an AUC of 0.9965. The segmentation model for nucleus and cytoplasm yielded a precision–recall AUC of 0.9721, with bounding box errors predominantly within 5%, ensuring reliable morphologic quantification. The accuracy of both the classification and subcellular segmentation models provided a robust foundation for the downstream extraction of morphological features. Comparative analysis of plasma cell morphology between MM and PCL patients revealed significantly elevated nucleus-to-plasma (N:C) ratios and decreased nuclear eccentricity in the PCL cohort. Mann-Whitney U tests further identified sex-associated differences in morphology among MM patients, with male patients exhibiting higher N:C ratios and lower eccentricity compared to females. Moreover, both N:C ratio and nuclear eccentricity were negatively correlated with hemoglobin levels, indicating that plasma cells with higher N:C ratios and lower eccentricity were associated with more severe anemia. Multivariate Cox regression analysis based on available survival data demonstrated that a higher N:C ratio was independently associated with inferior progression-free survival (HR = 2.12; 95% CI: 1.38-3.29; p = 0.042), while increased nuclear eccentricity served as a protective factor (HR = 0.73; 95% CI: 0.38–0.85; p = 0.035). The overall model was statistically significant with a concordance index (C-index) of 0.72.Conclusions: This study demonstrates that quantification of plasma cell morphology from bone marrow smears enables precise measurement of nucleus-to-plasma ratio and nuclear eccentricity. Notably, higher nucleus-to-plasma ratio and lower nuclear eccentricity were associated with more aggressive disease features and inferior hematologic profiles. Furthermore, these morphologic parameters independently predicted patient survival in MM, highlighting their potential as novel prognostic biomarkers. Integration of AI-assisted morphologic assessment into routine diagnostics may enhance risk stratification and guide therapeutic decision-making in clinical practice.