DIFFERENTIAL IMPACT OF CD34+ CELL DOSE FOR DIFFERENT AGE GROUPS IN ALLOGENEIC HEMATOPOIETIC CELL TRANSPLANTATION FOR ACUTE LEUKEMIA: A MACHINE LEARNING-BASED DISCOVERY.

Abstract

Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies, yet carries associated risks and complications. Continuous research focuses on predicting outcomes and identifying risk factors. Notably, the influence of CD34+ cell dose on overall survival (OS) has been the subject of numerous studies yielding contradictory results. We developed machine learning (ML) models to predict allo-HCT outcomes and through the application of SHAP (SHapley Additive exPlanations), an explainable artificial intelligence (XAI) technique, enabled the identification of new and clinically relevant feature-outcome relationships. In particular, we identified clear interaction between CD34+ cell dose of peripheral blood stem cell (PBSC) grafts and patient age at allo-HCT for acute leukemia patients. Results of multivariable analysis validated the interaction effect: On young acute leukemia patients (≤45-year-old), low dose of CD34+ cells (<4.3 × 10⁶ CD34+/kg) was associated with better OS against high dose (≥7 × 10⁶ CD34+/kg) (hazard ratio [HR] 0.38, p=0.019), while for older acute leukemia patients (>45-year-old), low CD34+ cell dose (<3.8 × 10⁶ CD34+/kg) was associated with worse OS against high dose (≥6.1 × 10⁶ CD34+/kg) (HR 1.58, P = 0.033). In conclusion, our findings suggest that tailoring CD34+ cell dose by patient age may benefit acute leukemia patients undergoing allo-HCT, while XAI showcases excellent proficiency in revealing such interactions.