Logistic regression models of cytokines in differentiating vitreoretinal lymphoma from uveitis.

BACKGROUND

Vitreoretinal lymphoma (VRL) can commonly masquerade as chronic idiopathic uveitis due to its nonspecific clinical presentation. Thus, its early diagnosis is difficult. In this study, new logistic regression models were used to classify VRL and uveitis. Additionally, the diagnostic performance of interleukin (IL)-10, the IL-10/IL-6, and the Interleukin Score for IntraOcular Lymphoma Diagnosis (ISOLD) are evaluated.

METHODS

Sixty-nine aqueous humors (AH) (46 VRL, 23 uveitis) and 65 vitreous humors (VH) (49 VRL, 16 uveitis) were collected from a single-center retrospective cohort. Logistic regression models were conducted based on IL-6 and IL-10. The cut-off values, area under the receiver operating characteristic curve (ROC) curve (AUC), sensitivity and specificity of IL-10, the IL-10/IL-6, the ISOLD, and the models were calculated from the ROC. Furthermore, Spearman's rank correlation analysis was performed to determine cytokine levels in VH and AH.

RESULTS

We redefined the cut-off values of IL-10, the IL-10/IL-6, the ISOLD, and the logistic regression models. In AH, the AUC values of IL-10, ISOLD, IL10/IL6, and the model were 0.91, 0.953, 0.952, and 0.967. In VH, they were 0.93, 0.95, 0.954, and 0.954, respectively. IL-6 (r = 0.7844) and IL-10 (r = 0.8506) in AH and VH showed a strong correlation.

CONCLUSIONS

IL-6 and IL-10 levels were introduced into new logistic regression models. The diagnostic efficacy of the models improved compared to the indicators mentioned above among Chinese patients. Additionally, the models could predict the probability of VRL more accurately. A strong correlation of cytokine levels showed the great potential of AH as prioritized auxiliary diagnostic for VRL.