Machine Learning to Predict the Occurrence of Distant Organ Involvement in Primary Lymphoma of Bone: A Population-Based Study Using the SEER Database

Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v7i3.5557

Shengting Yang1, Jiaji Ren2

1. University of Shanghai for Science and Technology, Shanghai 200093, China
2. Shandong Second Medical University, Weifang 261000, Shandong, China

Abstract

Primary lymphoma of bone (PLB) has a substantially poorer prognosis when distant organ involvement is present, making early risk identification clinically important. We used SEER data to develop and validate machine-learning models for predicting distant organ involvement in PLB and applied SHAP to interpret key predictors. A web-based calculator was built from the best-performing model. Among 690 analyzed patients, the Random Forest (RF) model showed the best performance, with an internal-validation accuracy of 0.852 and AUC of 0.907. This RF-based model may support individualized risk assessment and clinical decision-making in PLB.

Keywords

primary lymphoma of bone, metastasis, SEER, machine learning, prediction model

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