A Nomogram for Predicting the Diagnosis of Central Malignant Tumors Based on Preoperative Clinical Characteristics and Laboratory Indicators:

The purpose of this study is to develop a nomogram to predict the diagnostic probability of preoperative central lymphoma and glioma, as well as the diagnostic probability of glioblastoma and non glioblastoma in central malignant tumors. The author retrospectively analyzed patients with central lymphoma and glioma who received treatment in the neurosurgery department of Guangdong Provincial People's Hospital from 2016 to 2024. Eligible patients were randomly divided into training and validation sets in a 7:3 ratio. By integrating the least absolute shrinkage and selection operator \[LASSO\] and multivariate logistic regression analysis, the key variables for establishing a nomogram were identified. Moreover, seven models including logistic regression, decision tree, random forest, support vector machine (SVM), neural network, XGBoost, and lightGBM were used to calculate the area under the receiver operating characteristic curve (AUC)

Sex

All

Inclusion Criteria:

\- Patients who underwent surgical treatment and were diagnosed with PNCSL or glioma after surgery;

Exclusion Criteria:

\- Patients who have not undergone their first surgery