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

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 cytogenetic features. The model achieved an area under the receiver operating characteristic curve of 0.780, with enhanced performance after integrating PubMedBERT to extract insights from unstructured text data from bone marrow examination reports. Additionally, we applied SHapley Additive exPlanations to generate individualized risk scores, estimate progression probabilities, and visualize key predictive features, enabling personalized risk assessment. In conclusion, we developed a machine learning-based model predicting ICUS progression to myeloid malignancies. This model could serve as a valuable tool for personalized risk stratification and tailored patient monitoring in clinical practice.