Predictive modeling of outcomes in acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation using machine learning techniques

Background

Leukemia necessitates continuous research for effective therapeutic techniques. Acute leukemia (AL) patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) focus on key outcomes such as overall survival (OS), relapse, and graft-versus-host disease (GVHD).

Objective

This study aims to evaluate the capability of machine learning (ML) models in predicting OS, relapse, and GVHD in AL patients post-allo-HSCT.

Methods

Clinical data from 1243 AL patients, with 10 years of follow-up, was utilized to develop 28 ML models. These models incorporated four feature selection methods and seven ML algorithms. Model performance was assessed using the concordance index (c-index) with multivariate analysis.

Results

The multivariate model analysis showed the best FS/ML combinations were UCI_GLMN, IBMA_GLMN and IBMA_CB for OS, UCI_ST, UCI_RSF, UCI GLMB, UCI_GB, UCI_CB, MI_GLMN, IBMA_ST and IBMA GB for relapse, IBMA_GB for aGVHD and Boruta_GB for cGVHD (all p values < 0.0001, mean C-indices in 0.61–0.68)).

Conclusion

ML techniques, when combined with clinical variables, demonstrate high accuracy in predicting OS, relapse, and GVHD in AL patients.