# Multifactorial analysis and construction of a nomogram model for postoperative recurrence of glomus jugulare tumor

## TL;DR

This study creates a predictive model to help doctors assess the risk of glomus jugulare tumor recurrence after surgery.

## Contribution

A novel nomogram model using logistic regression was developed and validated for predicting postoperative recurrence of glomus jugulare tumors.

## Key findings

- Age, Ki-67, S-100 expression, and tumor invasion extent were identified as independent predictors of recurrence.
- The nomogram model demonstrated strong predictive accuracy with AUC values of 0.863 in the training set and 0.784 in the test set.
- The model was validated internally and externally, showing its reliability for clinical risk stratification.

## Abstract

To derive and validate a prognostic nomogram for predicting postoperative recurrence in patients with glomus jugulare tumor(GJT) to assist clinical decision-making.

A retrospective analysis was conducted on the clinical data of a total of 318 patients diagnosed with GJT at a single tertiary medical center. The study collected information on patient demographics, clinical symptoms and signs, examination results, and the extent of tumor growth. Patients were categorized into two groups based on DFS (Disease - free survival): those who experienced recurrence and those who did not. A nomogram model was developed using logistic regression to analyze the risk of postoperative recurrence.

Multivariate logistic regression analysis identified age, immunohistochemical expression levels of Ki-67 and S-100 and tumor invasion extent were significantly associated as independent predictors. These independent predictors were incorporated into a nomogram. The logistic regression-based nomogram showed excellent predictive accuracy of the nomogram model in the training set, validation set, and test set, with corresponding areas under the curve (AUC) of 0.863, 0.711, and 0.784, respectively.

The nomogram effectively predicts GJT recurrence, validated internally and externally, aiding clinical risk stratification.