Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning

Eckardt JN, Röllig C, Metzeler K, Kramer M, Stasik S, Georgi JA, Heisig P, Spiekermann K, Krug U, Braess J, Görlich D, Sauerland CM, Woermann B, Herold T, Berdel WE, Hiddemann W, Kroschinsky F, Schetelig J, Platzbecker U, Müller-Tidow C, Sauer T, Serve H, Baldus C, Schäfer-Eckart K, Kaufmann M, Krause S, Hänel M, Schliemann C, Hanoun M, Thiede C, Bornhäuser M, Wendt K, Middeke JM (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Book Volume: 108

Pages Range: 690-704

Journal Issue: 3

DOI: 10.3324/haematol.2021.280027

Abstract

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.

Authors with CRIS profile

Involved external institutions

Universitätsklinikum Carl Gustav Carus Dresden Germany (DE) Universitätsklinikum Leipzig Germany (DE) Technische Universität Dresden Germany (DE) Klinikum der Universität München Germany (DE) Klinikum Leverkusen Germany (DE) Krankenhaus Barmherzige Brüder Germany (DE) Westfälische Wilhelms-Universität (WWU) Münster Germany (DE) Charité - Universitätsmedizin Berlin Germany (DE) Universitätsklinikum Münster Germany (DE) Universitätsklinikum Heidelberg Germany (DE) Goethe-Universität Frankfurt am Main Germany (DE) Paracelsus Medizinische Privatuniversität, Nürnberg Germany (DE) Robert-Bosch-Krankenhaus Germany (DE) Universitätsklinikum Essen Germany (DE) Universitätsklinikum Schleswig-Holstein (UKSH) Germany (DE) Klinikum Chemnitz Germany (DE)

How to cite

APA:

Eckardt, J.N., Röllig, C., Metzeler, K., Kramer, M., Stasik, S., Georgi, J.A.,... Middeke, J.M. (2023). Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Haematologica, 108(3), 690-704. https://doi.org/10.3324/haematol.2021.280027

MLA:

Eckardt, Jan Niklas, et al. "Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning." Haematologica 108.3 (2023): 690-704.

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