| Autor: |
Eckardt JN; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden. jan-niklas.eckardt@uniklinikum-dresden.de., Röllig C; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Metzeler K; Medical Clinic and Policlinic I Hematology and Cell Therapy. University Hospital, Leipzig., Kramer M; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Stasik S; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Georgi JA; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Heisig P; Institute of Software and Multimedia Technology, Technical University Dresden, Dresden., Spiekermann K; Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich., Krug U; Medical Clinic III, Hospital Leverkusen, Leverkusen., Braess J; Hospital Barmherzige Brueder Regensburg, Regensburg., Görlich D; Institute for Biometrics and Clinical Research, University Muenster, Muenster., Sauerland CM; Institute for Biometrics and Clinical Research, University Muenster, Muenster., Woermann B; Department of Hematology, Oncology and Tumor Immunology, Charité, Berlin., Herold T; Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich., Berdel WE; Department of Internal Medicine A, University Hospital Muenster, Muenster., Hiddemann W; Laboratory for Leukemia Diagnostics, Department of Medicine III, University Hospital, LMU Munich, Munich., Kroschinsky F; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Schetelig J; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden., Platzbecker U; Medical Clinic and Policlinic I Hematology and Cell Therapy. University Hospital, Leipzig., Müller-Tidow C; Department of Medicine V, University Hospital Heidelberg, Heidelberg, Germany; German Consortium for Translational Cancer Research DKFZ, Heidelberg., Sauer T; Department of Medicine V, University Hospital Heidelberg, Heidelberg., Serve H; Department of Medicine 2, Hematology and Oncology, Goethe University Frankfurt, Frankfurt., Baldus C; Department of Hematology and Oncology, University Hospital Schleswig Holstein, Kiel., Schäfer-Eckart K; Department of Internal Medicine 5, Paracelsus Medical Private University Nuremberg, Nuremberg., Kaufmann M; Department of Hematology, Oncology and Palliative Care, Robert-Bosch Hospital, Stuttgart., Krause S; Department of Internal Medicine 5, University Hospital Erlangen, Erlangen., Hänel M; Department of Internal Medicine 3, Klinikum Chemnitz GmbH, Chemnitz, Germany; Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen., Schliemann C; Department of Internal Medicine A, University Hospital Muenster, Muenster., Hanoun M; Department of Internal Medicine 3, Klinikum Chemnitz GmbH, Chemnitz, Germany; Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Essen., Thiede C; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany; German Consortium for Translational Cancer Research DKFZ, Heidelberg., Bornhäuser M; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany; German Consortium for Translational Cancer Research DKFZ, Heidelberg, Germany; National Center for Tumor Diseases (NCT), Dresden., Wendt K; Medical Clinic and Policlinic I Hematology and Cell Therapy. University Hospital, Leipzig., Middeke JM; Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden. |
| Abstrakt: |
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. |
