Christoph Röllig

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

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 image...

Functional characterization of snoRNA-derived RNA (sdRNA) expression in healthy hematopoiesis and acute myeloid leukemia

Abstract SnoRNAs are highly expressed in AML and have implications in leukemogenesis and leukemic maintenance. SnoRNAs can be further processed into snoRNA-derived RNAs (sdRNAs). The role of sdRNAs in AML and healthy hematopoiesis remains largely elusive. We characterized sdRNA a...

Machine learning provides individualized prediction of outcomes after first complete remission without allo‐HSCT consolidation in adult acute myeloid leukemia—A HARMONY study

Abstract Allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is a curative treatment option for a significant proportion of patients with acute myeloid leukemia (AML), and it is generally recommended when the relapse risk without allo‐HSCT outweighs the estimated non‐r...