Utility and performance of cerebrospinal fluid cytology in discriminating central nervous system infections and brain tumors.

BACKGROUND AND OBJECTIVE

Differentiating central nervous system infections (CNSIs) from brain tumors (BTs) is difficult due to overlapping features and the limited individual indicators, and cerebrospinal fluid (CSF) cytology remains underutilized. To improve differential diagnosis, we developed a model based on 9 early, cost-effective cerebrospinal fluid parameters, including CSF cytology.

METHODS

Patients diagnosed with CNSIs or BTs at Xiangya Hospital of Central South University between October 1st, 2017 and March 31st, 2024 were enrolled and divided into the training set and the test set. Lasso analysis, random forest, and multivariable logistic regression were used to construct a diagnostic model to distinguish CNSIs from BTs by utilizing differences in basic CSF parameters and CSF cytology results. And its diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. A nomogram was used for model visualization.

RESULTS

A total of 783 patients were included in this study. 9 important CSF parameters significantly contribute to the differentiation between CNSIs and BTs, including CSF pressure, protein, glucose, adenosine deaminase, chloride, and the counts of lymphocytes, monocytes, plasma cells and phagocytes. CSF phagocytes and monocytes were elevated in BTs, whereas lymphocytes and plasma cells were higher in CNSIs. The model demonstrated strong diagnostic performance, achieving an area under the ROC curve (AUC) of 0.889 in the training set and 0.900 in the test set.

CONCLUSIONS

We developed a diagnostic model based on 9 CSF indicators. In our study, CSF phagocytes and monocytes were associated with BTs, while lymphocytes and plasma cells indicated CNSIs.