Machine learning-based prediction of progression from idiopathic cytopenia of undetermined significance to myeloid malignancies.

ABSTRACT: Although the risk of progression varies, a subset of patients with idiopathic cytopenia of undetermined significance (ICUS) eventually develop myeloid malignancies. Early identification of high-risk patients is crucial for timely intervention and optimized clinical management. This study aimed to develop a machine learning-based model to predict the progression of ICUS to myeloid malignancies. We retrospectively analyzed data from 1274 patients who underwent bone marrow examination at Asan Medical Center, Seoul, South Korea, between January 2000 and December 2021 and met the diagnostic criteria for ICUS. Among these patients, 36 (2.82%) progressed to myeloid malignancies. We developed a predictive model using the extreme gradient boosting algorithm, incorporating clinical, laboratory, and

SUBMITTER: Park H 

PROVIDER: S-EPMC12823607 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature