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

Jingyi Ren¹Jinbo Lu^2*Xun Shi¹Yuexin Cheng²

• ¹Department of Nuclear Medicine, Yancheng No 1 People's Hospital, Yancheng, China
• ²Department of Hematology, Yancheng No 1 People's Hospital, Yancheng, China

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 (SUVmax 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 SUVmax 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 SUVmax 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.