@article{Eckardt_Röllig_Metzeler_Kramer_Stasik_Georgi_Heisig_Spiekermann_Krug_Braess_Görlich_Sauerland_Woermann_Herold_Berdel_Hiddemann_Kroschinsky_Schetelig_Platzbecker_Müller-Tidow_Sauer_Serve_Baldus_Schäfer-Eckart_Kaufmann_Krause_Hänel_Schliemann_Hanoun_Thiede_Bornhäuser_Wendt_Middeke_2023, place={Pavia, Italy}, title={Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning}, volume={108}, url={https://haematologica.org/article/view/haematol.2021.280027}, DOI={10.3324/haematol.2021.280027}, abstractNote={<p>Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77–0.86 and between 0.63–0.74, respectively in our test set, and between 0.71–0.80 and 0.65–0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.</p&gt;}, number={3}, journal={Haematologica}, author={Eckardt, Jan-Niklas and Röllig, Christoph and Metzeler, Klaus and Kramer, Michael and Stasik, Sebastian and Georgi, Julia-Annabell and Heisig, Peter and Spiekermann, Karsten and Krug, Utz and Braess, Jan and Görlich, Dennis and Sauerland, Cristina M. and Woermann, Bernhard and Herold, Tobias and Berdel, Wolfgang E. and Hiddemann, Wolfgang and Kroschinsky, Frank and Schetelig, Johannes and Platzbecker, Uwe and Müller-Tidow, Carsten and Sauer, Tim and Serve, Hubert and Baldus, Claudia and Schäfer-Eckart, Kerstin and Kaufmann, Martin and Krause, Stefan and Hänel, Mathias and Schliemann, Christoph and Hanoun, Maher and Thiede, Christian and Bornhäuser, Martin and Wendt, Karsten and Middeke, Jan Moritz}, year={2023}, month={Mar.}, pages={690-704} }