Prediction of Lymphoma Bone Marrow Infiltration by Machine Learning Models Based on Fluorine-18 Fluorodeoxyglucose Positron Emission Computed Tomography.

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Abstract

Patients with lymphoma with bone marrow infiltration have a poor prognosis, and reliable bone marrow infiltration confirmation methods are critical for clinical management. This study aimed to construct machine learning models to identify bone marrow infiltration-influencing factors and predict lymphoma bone marrow infiltration using multimodal clinical data. A retrospective study included 243 bone marrow infiltration and 541 non-bone marrow infiltration patients with lymphoma. Eight machine learning algorithms were used to build models based on clinical characteristics, laboratory tests, and fluorine-18fluorodeoxyglucose Positron emission computed tomography (PET/CT) data. Model performance was evaluated by AUC and accuracy. A sensitivity analysis was performed in the pathology-confirmed bone marrow infiltration cohort to assess potential incorporation bias. Platelet count, hemoglobin, serum lactate dehydrogenase, lymph node involvement distribution, bone infiltration characteristics, and maximum standardized uptake value (SUV_max) of sternum, SUV_max of thoracic vertebra, SUV_max of lumbar vertebra, SUV_max of sacrum, SUV_max of femur correlated with lymphoma bone marrow infiltration. The random forest (RF) model showed the best performance (AUC = 0.842, accuracy = 80.9%) in the testing set. In pathology-confirmed subgroup analysis, the RF model maintained stable performance (AUC = 0.846, accuracy = 82.5%). The RF model is a powerful tool for lymphoma bone marrow infiltration prediction (AUC = 0.774, accuracy = 80.3%) using routine clinical, laboratory and PET/CT data, providing a promising approach for bone marrow infiltration assessment, PET/CT interpretation and quantitative risk stratification.

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