Cleveland Clinic Cancer Center (Taussig) Outcomes
Leukemia & Myeloid Disorders
A Personalized Prediction Model to Risk Stratify Patients With Acute Myeloid Leukemia Using Artificial Intelligence
Acute myeloid leukemia is a heterogeneous clonal disorder that is characterized by the accumulation of complex genomic alterations that affect disease biology and outcomes. Despite significant advanced in our understanding of the impact of these mutations on overall survival, established AML risk stratification guidelines are based primarily on cytogenetic analyses and a limited number of genes, don't take into account the complexity and the interaction between these mutations, and how particular constellations of genomic and clinical risk factors affect patient outcomes. A novel prognostic model was developed that incorporates clinical, cytogenetic, and mutational data to determine personalized outcomes specific to a particular patient.
Personalized Prediction for AML Patients Based on the New Model
gender = 2 = females, AML Subtype = 2 = secondary AML, Cyto-Grouping per ELN Criteria = 2 = intermediate risk, Cyto-Grouping per ELN Criteria = 3 = unfavorable risk, HCT = hematopietic stem cell transplant, HCT = 1 = yes, BM_Blasts % = bone marrow blast percentage
Genomic alterations have a differential impact on overall survival in each cytogenetic risk group, highlighting the complexity of incorporating these mutations into risk stratification. A personalized prediction model based on clinical-genomic data can accurately provide survival unique to each individual patient and can significantly outperform ELN classification or any currently available models. To ease the translation of this model into the clinic, a web application is currently under development and will be publicly available for use.