Age‐stratified machine learning identifies divergent prognostic significance of molecular alterations in AML

Age‐stratified machine learning identifies divergent prognostic significance of molecular alterations in AML

Abstract Risk stratification in acute myeloid leukemia (AML) is driven by genetics, yet patient age substantially influences therapeutic decisions. To evaluate how age alters the prognostic impact of genetic mutations, we pooled data from 3062 pediatric and adult AML patients from multiple cohorts....

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Main Authors: Jan‐Niklas Eckardt, Waldemar Hahn, Rhonda E. Ries, Szymon D. Chrost, Susann Winter, Sebastian Stasik, Christoph Röllig, Uwe Platzbecker, Carsten Müller‐Tidow, Hubert Serve, Claudia D. Baldus, Christoph Schliemann, Kerstin Schäfer‐Eckart, Maher Hanoun, Martin Kaufmann, Andreas Burchert, Johannes Schetelig, Martin Bornhäuser, Markus Wolfien, Soheil Meshinchi, Christian Thiede, Jan Moritz Middeke
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:HemaSphere
Online Access:https://doi.org/10.1002/hem3.70132
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https://doi.org/10.1002/hem3.70132

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