{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:31:38Z","timestamp":1788449498767,"version":"build-2803163510"},"reference-count":68,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["HemaSphere"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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. Signaling pathway mutations dominated in younger patients, while mutations in epigenetic regulators, spliceosome genes, and\n                    <jats:italic>TP53<\/jats:italic>\n                    alterations became more frequent with increasing age. Machine learning models were trained to identify prognostic variables and predict complete remission and 2\u2010year overall survival, achieving area\u2010under\u2010the\u2010curve scores of 0.801 and 0.791, respectively. Using Shapley (SHAP) values, we quantified the contribution of each variable to model decisions and traced their impact across six age groups: infants, children, adolescents\/young adults, adults, seniors, and elderly. The highest contributions to model decisions among genetic variables were found for alterations of\n                    <jats:italic>NPM1<\/jats:italic>\n                    ,\n                    <jats:italic>CEBPA<\/jats:italic>\n                    , inv(16), and t(8;21) conferring favorable risk and alterations of\n                    <jats:italic>TP53, RUNX1, ASXL1<\/jats:italic>\n                    , del(5q), \u20107, and \u201017 conferring adverse risk, while\n                    <jats:italic>FLT3<\/jats:italic>\n                    \u2010ITD had an ambiguous role conferring favorable treatment responses yet poor overall survival. Age significantly modified the prognostic value of genetic alterations, with no single alteration consistently predicting outcomes across all age groups. Specific alterations associated with aging such as\n                    <jats:italic>TP53<\/jats:italic>\n                    ,\n                    <jats:italic>ASXL1<\/jats:italic>\n                    , or del(5q) posed a disproportionately higher risk in younger patients. These results challenge uniform risk stratification models and highlight the need for context\u2010sensitive AML treatment strategies.\n                  <\/jats:p>","DOI":"10.1002\/hem3.70132","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T07:20:26Z","timestamp":1746602426000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Age\u2010stratified machine learning identifies divergent prognostic significance of molecular alterations in AML"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3649-2823","authenticated-orcid":false,"given":"Jan\u2010Niklas","family":"Eckardt","sequence":"first","affiliation":[{"name":"Department of Internal Medicine I University Hospital Carl Gustav Carus, TUD Dresden University of Technology Dresden Germany"},{"name":"Else Kr\u00f6ner Fresenius Center for Digital Health TUD Dresden University of Technology Dresden 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D.","family":"Chrost","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine I University Hospital Carl Gustav Carus, TUD Dresden University of Technology Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4228-4537","authenticated-orcid":false,"given":"Susann","family":"Winter","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine I University Hospital Carl Gustav Carus, TUD Dresden University of Technology Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-4894","authenticated-orcid":false,"given":"Sebastian","family":"Stasik","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine I University Hospital Carl Gustav Carus, TUD Dresden University of Technology Dresden 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