Prediction of Lymphoma Bone Marrow Infiltration by Machine Learning Models Based on Fluorine-18 Fluorodeoxyglucose Positron Emission Computed Tomography.
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 (SUVmax) of sternum, SUVmax of thoracic vertebra, SUVmax of lumbar vertebra, SUVmax of sacrum, SUVmax 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.
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 (SUVmax) of sternum, SUVmax of thoracic vertebra, SUVmax of lumbar vertebra, SUVmax of sacrum, SUVmax 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.