Deep learning predicts IDH1 mutation status in acute myeloid leukemia from bone marrow smears

Abstract

Abstract Acute myeloid leukemia (AML) with IDH1 mutations is amenable to targeted therapy, highlighting the need for reliable identification. We developed an attention-based multiple-instance learning model to predict IDH1 mutation status using bone marrow smear whole-slide images from 436 AML patients across four centers in Germany and the US, achieving a mean single pooled ROC-AUC of 0.81. In top tile analysis, model predictions for IDH1 were associated with immature leukemia phenotypes.