A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable Measurable Residual Disease (MRD) in Transplant-Eligible Multiple Myeloma (MM)

INTRODUCTION: There is expectation of using biomarkers to personalize treatment in MM. Yet, a successful treatment selection cannot be confirmed before 5 or 10 years of progression-free survival (PFS). Treatment individualization based on the probability of an individual patient to achieve undetectable MRD with a singular regimen, could represent a new model towards personalized treatment with fast assessment of its success. This idea has not been investigated previously.