Ethical considerations
This study received ethical approval from the institutional review boards of the participating institutions: Huashan Hospital of Fudan University (2023-515), Xuhui Central Hospital (2018025), the Eye and ENT Hospital of Fudan University (2020[2020013]), and Wanbei Coal-Electricity Group General Hospital (WBZY-LLWYH-2024-21). The study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Participants. Given that the multicentre case–control study was noninterventional and retrospective in nature, the institutional review boards waived the requirement for informed consent. In the prospective hospital-based screening phase, all participants provided written informed consent, signed before sample collection and study enrollment. Sex of participants was recorded as male or female according to hospital registration data, which were based on self-reported information at the time of admission. Due to the limited sample size, no sex- and/or gender-based analyses were performed.
Study design and participants
The PVRL screening model was developed through a multicentre, case‒control study in which patients were systematically identified and categorized based on predefined diagnostic criteria. This case‒control, multicentre study was conducted in 4 hospitals across China between January 1, 2016, and June 30, 2024. The discovery cohort comprised PVRL patients from the Eye and ENT Hospital of Fudan University. The first validation cohort included PVRL patients from Wanbei Coal-Electricity Group General Hospital and Xuhui Central Hospital of Fudan University, and the second validation cohort included PVRL patients from Huashan Hospital of Fudan University. The third validation cohort included PVRL patients with CNS involvement from Huashan Hospital of Fudan University. Healthy controls were recruited from the health examination centres of the same hospitals where the PVRL patients were enrolled. Furthermore, the healthy controls were matched to cases by age and gender. A one-to-one exact matching strategy was applied, with cases and controls matched on age and sex, to ensure comparable demographic characteristics between groups and to minimize potential confounding effects. Finally, 100 PVRL participants and 117 normal controls were included in the discovery cohort (Fig. S6A); 42 PVRL participants and 60 normal controls were included in the first validation cohort (Fig. S6B); 36 PVRL participants and 42 normal controls were included in the second validation cohort (Fig. S6C); and 77 PVRL-CNS participants and 73 normal controls were included in the third validation cohort (Fig. S6D).
Owing to the heterogeneous and often nonspecific clinical PVRL, it is frequently misdiagnosed as uveitis. To address this, a diagnostic differentiation cohort was established, consisting of 155 patients with PVRL and 158 with uveitis. Notably, the 155 PVRL patients overlapped with those in the previously described validation cohorts 1, 2, and 3. The uveitis patients were recruited from the Eye and ENT Hospital of Fudan University. Among the 158 patients with uveitis, 132 had endogenous, non-infectious causes, such as Fuchs uveitis, sarcoidosis uveitis, and Behçet uveitis; 26 cases had exogenous, infectious causes, including bacterial endophthalmitis–associated uveitis (n = 7) and herpetic uveitis (n = 19).
A follow-up cohort was established to assess dynamic changes in CBC parameters during PVRL treatment. In total, 29 PVRL patients were included, with overlap with the previously described PVRL patients from the Eye and ENT Hospital of Fudan University.
To further evaluate the diagnostic performance of CBC tests and interleukins in aqueous/vitreous humor for PVRL, multiple cohorts from the Eye and ENT Hospital and Huashan Hospital were included. Notably, these PVRL participants are also part of the previously described cohorts from the Eye and ENT Hospital and Huashan Hospital of Fudan University.
The performance of the PVRL screening model was validated through two large sample cohort studies: one hospital-based prospective cohort study and one community-based cross-sectional study.
A prospective screening program was initiated at the Eye and ENT Hospital of Fudan University. In brief, all patients presenting to the hospital were sequentially enrolled from October 2024 to May 2025. CBC tests were performed for all eligible participants. Individuals who screened positive for PVRL using the established screening model were further evaluated and confirmed by histopathological examination of biopsy samples. As a tertiary referral center for PVRL, the Eye and ENT Hospital of Fudan University receives a substantial number of patients with suspected disease. As a result, the prevalence of PVRL in this prospective screening program is significantly higher than that in the general population.
In parallel, a community-based cross-sectional screening program was launched in Xuhui District, Shanghai, in July 2024. Residents of Xuhui District were sequentially invited to participate through local community health service centres. CBC tests were conducted for all eligible participants, and those who screened positive for PVRL were referred for confirmatory diagnosis via histopathological analysis of biopsy samples. Due to the low prevalence of PVRL and its higher incidence in the elderly population, we limited the prospective screening program to individuals aged over 40 years to enrich the prevalence of PVRL within this community-based cross-sectional screening cohort. As a result, the prevalence of PVRL in this community-based cross-sectional screening program is significantly higher than that in the general population.
The detailed study design and participant information are provided in the Supplementary Materials. The study design is illustrated in Fig. 1A.
Diagnostic criteria for PVRL
The diagnosis of PVRL was established based on the 2016 World Health Organization classification of lymphoid neoplasms24. All patients with PVRL or CNS lymphoma (CNSL) involvement underwent biopsy procedures. PVRL was diagnosed based on positive pathological findings from vitreous biopsy, following established standards for vitreous sampling and biopsy-based diagnostic criteria9,25. Undiluted vitreous samples were obtained via dry vitrectomy and processed for cytological analysis. A positive pathological finding was defined as follows: conventional smear cytology demonstrating large lymphoid cells with irregularly shaped nuclei, multiple prominent nucleoli, and scant basophilic cytoplasm, typically accompanied by small reactive T lymphocyte infiltration, consistent with large lymphomatous cells.
CNSL was diagnosed when a positive pathological finding was obtained from CNS tissue biopsy. Patients in whom CNSL was ruled out at the time of PVRL diagnosis were classified as having isolated PVRL. Patients diagnosed simultaneously with CNSL were classified as having PVRL with concurrent CNS involvement. In accordance with the guidelines of the International Primary CNS Lymphoma Collaborative Group26 and the European Association of Neuro-Oncology27, all patients underwent 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) and/or bone marrow aspiration to exclude systemic lymphoma involvement.
Model development
In total, 30 features were employed to develop the diagnostic models. These features included: neutrophil count, neutrophil percentage, red blood cell count, thrombocytocrit, platelet count (PLT), PDW, HG, eosinophil count, eosinophil percentage, basophil count, basophil percentage, mean platelet volume, lymphocyte count, lymphocyte percentage, hematocrit, monocyte count, monocyte percentage, PLCR, white blood cell count, red blood cell distribution width—standard deviation, red blood cell distribution width—coefficient of variation, mean corpuscular volume, mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, platelet-to-lymphocyte ratio (PLT/lymphocyte count), neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, PLT × neutrophil-to-lymphocyte ratio, PLT × neutrophil × monocyte-to-lymphocyte ratio, and neutrophil × monocyte × PLT-to-lymphocyte ratio.
Initially, features exhibiting a correlation coefficient exceeding 0.8 were removed to reduce multicollinearity. In this study, all included variables showed correlations below 0.8. To handle missing values, the median imputation method was employed28. Twelve ML models—AdaBoost, DT, GLM, GBM, K-nearest neighbors, LightGBM, multilayer perceptron classifier, naïve Bayes, RF, support vector machine, TabNet, and XGBoost—were utilized, each leveraging its unique strengths to increase model diversity. To optimize the predictive models, we employed a two-stage hyperparameter tuning strategy. First, a grid search was used to define multiple candidate hyperparameter configurations. A nested 5-fold cross-validation was then performed for each candidate configuration in the discovery cohort. Specifically, the discovery cohort was evenly split into 5 folds, with 1 fold used as the validation set and the remaining four as the training set. Each candidate configuration was applied consistently across all 5 folds, meaning that the cross-validation was used to evaluate the performance of pre-specified hyperparameters rather than to generate different hyperparameters for each fold. The same hyperparameter settings were used to train the model on each of the five training folds and to evaluate it on the corresponding validation fold, resulting in five AUC values for each candidate configuration. Second, the mean-optimal configuration was identified by calculating the mean AUC across all folds for each candidate configuration. The configuration achieving the highest mean AUC was selected as the final hyperparameter set. Minor manual fine-tuning (e.g., adjustment of learning rate or max depth) was performed only around this final configuration to enhance stability. The final selection criterion remained the highest mean performance from the 5-fold cross-validation, ensuring the adoption of a robust and generalizable hyperparameter configuration. The final model, trained with the optimized hyperparameters, was then applied to the validation cohorts to generate the reported results. The final hyperparameters of these twelve ML models are shown in Table S10. Each method was chosen for its balance of simplicity, which allows for interpretable results and power, enabling the modeling of complex linear and nonlinear interactions among input features. Modeling was conducted in Python (v 3.11). The code is publicly available via GitHub–Zenodo and can be accessed using the https://zenodo.org/records/17189239.
Model performance evaluation
The models were assessed using various metrics, including sensitivity, specificity, PPV, NPV, F1 score, and accuracy. Their classification capabilities were further evaluated using the area under the receiver operating characteristic curve and the area under the precision‒recall curve. To compare the AUC values across different ML models, the DeLong test29, a nonparametric method, was utilized.
Feature selection and model explanation
In the discovery cohort, sample features were selected using the SHAP method30. Feature selection was performed to reduce the number of predictors while maintaining optimal model performance. In the discovery cohort, we applied a sequential forward-selection procedure guided by SHAP importance and statistical evaluation as follows: (1) ranking by SHAP importance: a model was trained on the full discovery cohort to compute mean SHAP values for all 30 features, generating a global importance ranking; (2) iterative model building: starting from the top-ranked feature, models were built incrementally by adding one feature at a time in descending SHAP order (top 1, top 2, …, up to all 30 features); (3) performance evaluation: in the discovery cohort, the AUC for each model was calculated using 5-fold cross-validation to obtain stable estimates; (4) statistical comparison: in the discovery cohort, each model’s performance was compared to that of the full 30-feature model using the DeLong test29; (5) final selection: to balance parsimony and predictive performance, we selected the model with the fewest features whose performance was statistically non-inferior or superior to that of the full 30-feature model. In the discovery cohort, during feature selection, the DeLong test29, a nonparametric method, was used to evaluate differences in AUC values before and after feature selection. The SHAP method supplies both global and local explanations for the model. The global explanation delivered consistent and precise attribution values for each feature, emphasizing the connections between the input features and PVRL. Conversely, the local explanation demonstrated specific predictions for individual PVRL cases on the basis of their respective input data.
Web-based model deployment
To facilitate clinical implementation, the final model was deployed as a web application using the Streamlit framework in Python. By inputting the relevant feature values, the application provides a probability estimate for PVRL and generates a personalized force plot, enhancing interpretability for individual cases.
Statistical analysis
All the statistical analyses were conducted using GraphPad Prism (version 10) and Python (version 3.11). The normality of the data distribution was assessed via the Kolmogorov–Smirnov test. For normally distributed continuous variables, paired or independent Student’s t tests were applied, whereas the Kruskal–Wallis test was used for nonnormally distributed data. Categorical variables were analysed using the chi-square test where appropriate. Continuous variables are reported as the means ± standard deviations (SDs), and categorical variables are expressed as counts and percentages. ROC curve analysis was performed to evaluate the diagnostic value of interleukins. The AUC was calculated to assess the overall diagnostic performance. The optimal cutoff value was determined using the Youden index (sensitivity + specificity−1). Sensitivity and specificity corresponding to the optimal cutoff were also reported. A two-tailed P value < 0.05 was considered statistically significant.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
