Distinguishing lymphoma from benign lymphadenopathy in fever of unknown origin using a diagnostic model based on PET/CT and clinical-laboratory parameters.

backgroundFever of unknown origin (FUO) remains a complex diagnostic challenge, with infection, malignancy, and inflammatory diseases as the leading causes. A substantial proportion of FUO patients present with lymphadenopathy, among which lymphoma and benign lymph node disorders are the most critical differential diagnoses due to their markedly different management and prognosis. Although

resultsThis study retrospectively analyzed patients with FUO who underwent PET/CT. Lymph nodes were included if their short-axis diameter was ≥ 1 cm on axial CT or if metabolic activity exceeded that of the mediastinal blood pool. Volumes of interest were manually delineated in LIFEx using a 40% SUVmax threshold. Among 203 patients (114 with lymphoma and 89 with benign lymphadenopathy), logistic regression analyses identified independent predictors across three domains: hyperpyrexia, joint pain, and rash among clinical factors; albumin (ALB), procalcitonin (PCT), and serum amyloid A (SAA) among laboratory indicators; and SUVmean, total metabolic tumor volume (TMTV), and total lesion glycolysis (TLG) among PET parameters. A combined model incorporating rash, PCT, SAA, SUVmean, and TMTV achieved superior diagnostic performance, with an area under the ROC curve (AUC) of 0.965, significantly outperforming models based solely on clinical (AUC = 0.790), laboratory (AUC = 0.866), or PET (AUC = 0.845) variables (all P < 0.001). A nomogram was subsequently developed for individualized risk prediction.

conclusionsRash, PCT, SAA, SUVmean, and TMTV were independent predictors distinguishing lymphoma from benign lymphadenopathy in FUO. Integrating clinical, laboratory, and PET parameters markedly improved diagnostic accuracy.