{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:50:24Z","timestamp":1759333824816,"version":"build-2065373602"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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\u2014Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)\u2014were trained using these predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Lymphomatous nodes exhibited significantly elevated metabolic activity (SUV<jats:sub>max<\/jats:sub> median: 16.0 vs. 10.0, P&amp;lt;0.001), larger short-axis diameters (13 mm vs. 11 mm, P&amp;lt;0.001), and concurrent splenic hypermetabolism (spleen SUV<jats:sub>max<\/jats:sub> 3.1 vs. 2.8, P&amp;lt;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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Integration of PET\/CT-derived SUV<jats:sub>max<\/jats:sub> 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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1643924","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:14:24Z","timestamp":1759205664000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The significance of PET\/CT combined with machine learning models for the classification of lymphoma involvement and metastases in enlarged lymph 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