Logistic Regression Classification of Primary Vitreoretinal Lymphoma versus Uveitis by Interleukin 6 and Interleukin 10 Levels.

Purpose: To assess the diagnostic performance and generalizability of logistic regression in classifying primary vitreoretinal lymphoma (PVRL) versus uveitis from intraocular cytokine levels in a single-center retrospective cohort, comparing a logistic regression model and previously published Interleukin Score for Intraocular Lymphoma Diagnosis (ISOLD) scores against the interleukin 10 (IL-10)-to-interleukin 6 (IL-6) ratio.

Design: Retrospective cohort study.

Participants: Patient histories, pathology reports, and intraocular cytokine levels from 2339 patient entries in the National Eye Institute Histopathology Core database.

Methods: Patient diagnoses of PVRL versus uveitis and associated aqueous or vitreous IL-6 and IL-10 levels were collected retrospectively. From these data, cytokine levels were compared between diagnoses with the Mann-Whitney U test. A logistic regression model was trained to classify PVRL versus uveitis from aqueous and vitreous IL-6 and IL-10 samples and compared with ISOLD scores and IL-10-to-IL-6 ratios.

Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) for each classifier and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) at the optimal cutoff (maximal Youden index) for each classifier.

Results: Seventy-seven lymphoma patients (10 aqueous samples, 67 vitreous samples) and 84 uveitis patients (19 aqueous samples, 65 vitreous samples) treated between October 5, 1999, and September 16, 2015, were included. Interleukin 6 levels were higher and IL-10 levels were lower in uveitis patients compared with lymphoma patients (P < 0.01). For vitreous samples, the logistic regression model, ISOLD score, and IL-10-to-IL-6 ratio achieved AUCs of 98.3%, 97.7%, and 96.3%, respectively. Sensitivity, specificity, PPV, and NPV at the optimal cutoffs for each classifier were 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9% for the ISOLD score; and 94.2%, 95.3%, 95.6%, and 93.9% for the IL-10-to-IL-6 ratio. All models achieved complete separation between uveitis and lymphoma in the aqueous data set.

Conclusions: The accuracy of the logistic regression model and generalizability of the ISOLD score to an independent patient cohort suggest that intraocular cytokine analysis by logistic regression may be a promising adjunct to cytopathologic analysis, the gold standard, for the early diagnosis of primary vitreoretinal lymphoma. Further validation studies are merited.