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Single-Scan Machine Learning Prediction of Meningioma Tumor Growth Risk and Progression Using Neurosurgeon-Evaluated

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary
    This summary is machine-generated.

    Machine learning accurately predicts meningioma growth risk and volumetric rates using single imaging scans. This novel approach aids in personalized patient monitoring and potential early intervention for central nervous system tumors.

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    Area of Science:

    • Neuro-oncology
    • Medical imaging analysis
    • Machine learning applications

    Background:

    • Clinical monitoring of central nervous system (CNS) tumors, particularly meningiomas, is challenging due to limitations in predictive imaging tools.
    • Accurate assessment of meningioma growth and progression is crucial for effective clinical decision-making and patient management.
    • Current methods rely on serial imaging, but predicting tumor growth risks and rates remains difficult.

    Purpose of the Study:

    • To introduce a novel machine learning (ML) application for predicting meningioma growth risks (growing, stable, shrinking) and estimating volumetric growth rates.
    • To utilize neurosurgeon-assessed clinical features from a single imaging timepoint for non-invasive prediction.
    • To develop a reliable model independent of volumetric data for unbiased assessment.

    Main Methods:

    • Employed 12 clinical features (e.g., calcification, CSF plane, edema, location, T2 intensity, regularity, sex, ethnicity, age) derived from MRI and CT scans of 336 patients.
    • Applied machine learning models, including k-nearest neighbors (KNN), to predict meningioma growth risk and volumetric growth rate.
    • Utilized 5-fold and 10-fold cross-validation schemes to evaluate model performance.

    Main Results:

    • Machine learning models achieved high accuracy, exceeding 99%, in predicting meningioma growth risk and volumetric growth rates.
    • The k-nearest neighbors (KNN) model demonstrated superior performance compared to other tested ML models in both prediction tasks.
    • The study successfully predicted tumor behavior using only single-timepoint imaging features, without incorporating volumetric data.

    Conclusions:

    • Machine learning offers a powerful paradigm for predicting meningioma growth risk and progression.
    • This approach enables improved patient-specific tumor monitoring and facilitates opportunities for early intervention.
    • The findings highlight the potential of ML in enhancing the clinical management of meningiomas.