A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up

Abstract

Purpose: The selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up.

Methods: All patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence.

Results: Out of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 - 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001).

Conclusion: The proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting.

Keywords: classification; immunohistochemistry; meningioma; prediction model; preselection; progression; recurrence.

Figure 1

Flowchart and clinical use of proposed risk score.

Figure 2

ROC analysis (A) and calibration curve (B) of the final model.

Figure 3

P/R-free survival stratified by risk groups.

Figure 4

P/R-free survival curves for predictor variables previous surgery (A), microscopically necrosis (B), Simpson grade grouped (C) low (I, II and III) and high (IV and V), microscopically patternless growth (D) and progesterone receptor staining (E) stratified in low staining expression (negative and focally positive <50%) and high expression (>50%).