18F-FDG PET/CT metabolic parameters are correlated with clinical features and valuable in clinical stratification management in patients of castleman disease

Background Castleman disease (CD) is a rare lymphoproliferative disorder. This study is to evaluate the correlation between ¹⁸ F-FDG PET/CT and clinical features in CD patients, and exploring its value in distinguishing disease severity and assisting in risk stratification. Methods We retrospectively enrolled 93 patients with newly diagnosed CD. Traditional semi-quantitative ¹⁸ F-FDG PET/CT parameters including the maximum standardized uptake value (SUV _max ), total metabolic lesion volume (MLV), total lesion glycolysis (TLG) were measured, and the lymph node to liver ratio of SUV _max (LLR), lymph node to mediastinal blood pool of SUV _max (LMR), spleen to liver ratio of SUV _max (SLR) and No. of involved lymph node stations (LNS) were calculated. The correlation between these metabolic parameters and clinical features were studyed using a univariate analysis. The influencing factors of CD severity were determined by univariate and multivariate analysis. The optimal cut-off values for metabolic parameters were obtained by receiver operating characteristic (ROC) curve . Results A total of 20 unicentric CD (UCD) and 73 multicentric CD (MCD) cases were included, with the highest SUV _max of Lymph nodes ranged 1.40 ~ 28.18 (median, 4.86). The metabolic parameters (SUV _max , MLV, TLG, LLR, LMR, SLR) in MCD were significantly higher than those in UCD ( p  < 0.05). There were significant differences in MLV, TLG, LLR and SLR among different histological subtypes ( p  < 0.05). The No. of involved lymph node stations (LNS) and spleen-to-liver ratio (SLR) were significantly correlated with laboratory findings. In univariate and multivariate analyses, SLR ( p  = 0.011; OR value = 14.806) and HGB ( p  = 0.004; OR value = 0.044) exhibited an independent correlation with disease severity. The ROC curve revealed that SLR had a sensitivity of 77.4%, specificity of 69.4% and AUC of 0.761 (cut-off value = 1.04; p  < 0.001) in discriminating severity of CD. SLR also showed significant statistical differences between severe and non-severe iMCD ( p  = 0.016). Conclusions SLR is closely related to clinical features of CD, and can relatively effectively differentiate the severity of CD and assist in the clinical risk stratification of iMCD.