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. 2025 Apr 10;17(4):e82033.
doi: 10.7759/cureus.82033. eCollection 2025 Apr.

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

Simon G Ammanuel  1 , Matthew Stenerson  2 , Thomas Staniszewski  2 , Manasa Kalluri  2 , Benjamin Lee  2 , Elsa Nico  2 , Azam S Ahmed  2
Affiliations

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

Simon G Ammanuel et al. Cureus. .

Abstract

Objective Meningiomas commonly recur following gross total resection (GTR), and the risk of recurrence is difficult to predict using current classification schemes such as the World Health Organization (WHO) tumor grade. This study aimed to create a predictive model of recurrence risk following GTR of WHO grade 1 meningiomas based on histopathological and epidemiological factors. Methods A retrospective chart review was completed for all patients at our institution who underwent their first surgery for a WHO grade 1 meningioma between 2017 and 2022. Those with genetic predispositions, such as neurofibromatosis type 2, were excluded. Baseline characteristics, including histopathology findings, were obtained, and we used a Risk-calibrated Superspase Linear Integer Model (Risk-SLIM) with a five-fold cross-validation (CV) to create a predictive model of recurrence over an average follow-up of three years. Results Univariate analysis of our selected variables revealed a significant predictive association between WHO grade 1 meningioma recurrence and subtotal resection but not with any other variable. However, the meningioma recurrence score (MRS) generated by our machine learning algorithm revealed multiple predictive factors of recurrence, including age, female gender, and various histopathologic features, including the Ki-67/MIB-1 index. Conclusions Machine learning algorithms like the one we present here may help identify patients at high risk of recurrence of their WHO grade 1 meningioma, and they are more likely to benefit from closer postoperative surveillance or adjuvant treatment, even when GTR is achieved.

Keywords: artificial intelligence; grade 1 meningioma; machine learning; meningioma; neurosurgery.

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Conflict of interest statement

Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. University of Wisconsin issued approval 2015-1166. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. ROC curve for the Risk-SLIM model with a fivefold cross-validation AUC of 0.76 and fivefold cross-validation calibration error of 4.7%
ROC: receiver operating characteristic; Risk-SLIM: Risk-calibrated Superspase Linear Integer Model; AUC: area under the curve
See this image and copyright information in PMC

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