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Development of a Machine Learning Algorithm for the Prediction of WHO Grade 1 Meningioma Recurrence.

Simon G Ammanuel1, Matthew Stenerson2, Thomas Staniszewski2

  • 1Department of Neurological Surgery, University of Wisconsin Hospitals and Clinics, Madison, USA.

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|May 12, 2025
PubMed
Summary

Predicting recurrence of World Health Organization (WHO) grade 1 meningiomas after surgery is challenging. Machine learning identified key factors like age and Ki-67 index, improving risk assessment for better patient management.

Keywords:
artificial intelligencegrade 1 meningiomamachine learningmeningiomaneurosurgery

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Area of Science:

  • Neurosurgery
  • Oncology
  • Machine Learning in Medicine

Background:

  • Meningiomas frequently recur after gross total resection (GTR), with current classification systems like World Health Organization (WHO) tumor grade offering limited predictive accuracy for recurrence risk.
  • Accurate prediction of recurrence is crucial for optimizing patient surveillance and treatment strategies following surgical resection of WHO grade 1 meningiomas.

Purpose of the Study:

  • To develop a predictive model for recurrence risk in WHO grade 1 meningiomas post-GTR.
  • To identify histopathological and epidemiological factors associated with meningioma recurrence.

Main Methods:

  • Retrospective chart review of patients undergoing first-time surgery for WHO grade 1 meningioma (2017-2022), excluding those with genetic predispositions.
  • Application of a Risk-calibrated Superspase Linear Integer Model (Risk-SLIM) with five-fold cross-validation to predict recurrence over a three-year follow-up period.

Main Results:

  • Univariate analysis indicated subtotal resection as a significant predictor of recurrence, but no other single variable showed significance.
  • The developed meningioma recurrence score (MRS) using machine learning identified multiple predictive factors, including patient age, female gender, and histopathological features like the Ki-67/MIB-1 index.

Conclusions:

  • Machine learning models can effectively identify patients with WHO grade 1 meningiomas at high risk for recurrence, even after GTR.
  • These predictive models can guide decisions for closer postoperative surveillance or adjuvant therapy, potentially improving outcomes for high-risk individuals.