An Ensemble Model for Acute Myeloid Leukemia Risk Stratification Recommendations by Combining Machine Learning with Clinical Guidelines

Abstract

AbstractAcute Myeloid Leukemia (AML) is a complex disease requiring accurate risk stratification for effective treatment planning. This study introduces an innovative ensemble machine learning model integrated with the European LeukemiaNet (ELN) 2022 recommendations to enhance AML risk stratification. The model demonstrated superior performance by utilizing a compre- hensive dataset of 1,213 patients from National Taiwan University Hospital (NTUH) and an external cohort of 2,113 patients from UK-NCRI trials. On the external cohort, it improved a concordance index (c-index) from 0.61 to 0.64 and effectively distinguished three different risk levels with median hazard ratios ranging from 18% to 50% improved. Key insights were gained from the discovered significant features influencing risk prediction, including age, genetic mu- tations, and hematological parameters. Notably, the model identified specific cytogenetic and molecular alterations likeTP53, IDH2, SRSF2, STAG2, KIT, TET2, and karyotype (-5, -7, -15, inv(16)), alongside age and platelet counts. Additionally, the study explored variations in the effectiveness of hematopoietic stem cell transplantation (HSCT) across different risk levels, offering new perspectives on treatment effects. In summary, this study develops an ensemble model based on the NTUH cohort to deliver improved performance in AML risk stratification, showcasing the potential of integrating machine learning techniques with medical guidelines to enhance patient care and personalized medicine.