The significance of PET/CT combined with machine learning models for the classification of lymphoma involvement and metastases in enlarged lymph nodes

Objective Accurate differentiation between lymphoma involvement and lymph node metastasis poses significant diagnostic challenges due to overlapping imaging characteristics. This study evaluates the discriminative capacity of PET/CT metabolic profiling integrated with machine learning for nodal pathology classification. Methods We analyzed 247 lymph nodes from patients with diffuse large B-cell lymphoma (DLBCL, n=39) and solid tumor metastases (n=46). Multivariable logistic regression identified key PET/CT biomarkers, including metabolic parameters and anatomical features. Three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were trained using these predictors. Results Lymphomatous nodes exhibited significantly elevated metabolic activity (SUV max median: 16.0 vs. 10.0, P<0.001), larger short-axis diameters (13 mm vs. 11 mm, P<0.001), and concurrent splenic hypermetabolism (spleen SUV max 3.1 vs. 2.8, P<0.001). The RF model demonstrated exceptional performance with an AUC of 0.942, accuracy of 93.88%, and 100% specificity, outperforming SVM (AUC = 0.850) and ANN (AUC = 0.824). Splenic metabolic parameters significantly enhanced model discrimination. Conclusion Integration of PET/CT-derived SUV max and splenic metabolic features with machine learning, particularly RF algorithms, provides a potential framework for distinguishing lymphoma-involved from metastatic nodes. This approach holds promise for optimizing biopsy decisions and refining pretreatment risk stratification in clinical oncology.