AI Article Synopsis

  • The study aims to improve patient selection for further treatment after meningioma surgery by developing a predictive scoring system based on various clinical and histological factors.
  • Researchers analyzed data from 378 patients to identify key variables related to tumor recurrence and created a logistic regression model that categorizes patients into low, intermediate, and high-risk groups.
  • The predictive model demonstrated good accuracy in identifying patients at risk for recurrence, suggesting it can be a helpful tool for guiding further molecular testing and treatment decisions in a clinical setting.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646388PMC
http://dx.doi.org/10.3389/fonc.2023.1279933DOI Listing

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