An Unsupervised Machine Learning Method Stratifies Chronic Lymphocytic Leukemia Patients in Novel Categories with Different Risk of Early Treatment

Abstract:

Novel scoring systems have been developed in recent years to improve the accuracy of prognostication from historical clinical staging systems (Rai, Binet) for chronic lymphocytic leukemia (CLL). Most of them, however, rely on discretized and dichotomic values of the various biomarkers to infer prognosis. Here we analyzed the immunophenotypic and (immuno)genetic profiles in a wide CLL cohort by applying unsupervised machine learning methods elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional models.

Authors

Journal

Blood

Publication date

15/11/2022

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