Achievement of complete remission (CR) 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 individually tailor treatment concepts to disease biology. We used nine machine learning (ML) models to predict CR and 2-year overall survival (OS) in a large multi-center cohort of 1383 AML patients who received intensive induction therapy using clinical, laboratory, cytogenetic and molecular genetic data and validated our results on an external multicenter cohort. Our ML models autonomously selected predictive features both 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 (dm) CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin at initial diagnosis were statistically significant markers predictive of CR, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), dmCEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, TP53, age, white blood cell count, peripheral blast count, serum LDH and Hb at initial diagnosis as well as extramedullary manifestations were predictive for 2-year OS. For prediction of CR and 2-year OS, AUROCs ranged between 0.77 – 0.86 and 0.63 and 0.74, respectively in our test set and 0.71 – 0.80 and 0.65 – 0.75 in the external validation cohort. We demonstrate 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.
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