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Background <p>Fever 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 <sup>18</sup>F-FDG PET/CT plays an indispensable role in lymphoma detection, significant metabolic overlap between malignant and benign lymphadenopathy limits its standalone diagnostic accuracy in FUO. Therefore, integrating PET/CT metabolic parameters with clinical features and laboratory markers may improve the differentiation between lymphoma and benign lymphadenopathy, facilitating timely and accurate diagnosis in FUO patients.</p> Results <p>This study retrospectively analyzed patients with FUO who underwent PET/CT. Lymph nodes were included if their short-axis diameter was ≥ 1&#xa0;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 <i>P</i> &lt; 0.001). A nomogram was subsequently developed for individualized risk prediction.</p> Conclusions <p>Rash, 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Xinchao Zhang,
  • Yujing Hu,
  • Yalong Di,
  • Congna Tian,
  • Qiang Wei,
  • Ruming Zhou,
  • Jingjie Zhang,
  • Fenglian Jing,
  • Yanzhu Bian

摘要

Background

Fever 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 18F-FDG PET/CT plays an indispensable role in lymphoma detection, significant metabolic overlap between malignant and benign lymphadenopathy limits its standalone diagnostic accuracy in FUO. Therefore, integrating PET/CT metabolic parameters with clinical features and laboratory markers may improve the differentiation between lymphoma and benign lymphadenopathy, facilitating timely and accurate diagnosis in FUO patients.

Results

This 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.

Conclusions

Rash, 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.