1. Precision oncology in AML: validation of the prognostic value of the knowledge bank approach and suggestions for improvement
- Author
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Marius Bill, Krzysztof Mrózek, Brian Giacopelli, Jessica Kohlschmidt, Deedra Nicolet, Dimitrios Papaioannou, Ann-Kathrin Eisfeld, Jonathan E. Kolitz, Bayard L. Powell, Andrew J. Carroll, Richard M. Stone, Ramiro Garzon, John C. Byrd, Clara D. Bloomfield, and Christopher C. Oakes
- Subjects
Acute myeloid leukemia ,Knowledge bank ,Next-generation sequencing ,Gene mutations ,Clinical outcome ,Diseases of the blood and blood-forming organs ,RC633-647.5 ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Recently, a novel knowledge bank (KB) approach to predict outcomes of individual patients with acute myeloid leukemia (AML) was developed using unbiased machine learning. To validate its prognostic value, we analyzed 1612 adults with de novo AML treated on Cancer and Leukemia Group B front-line trials who had pretreatment clinical, cytogenetics, and mutation data on 81 leukemia/cancer-associated genes available. We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate the predictive values of the KB algorithm and other risk classifications. The KB algorithm predicted 3-year overall survival (OS) probability in the entire patient cohort (AUCKB = 0.799), and both younger (
- Published
- 2021
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