An Interpretable Machine Learning Model for Meningioma Grade Prediction

Ali Golbaf, Damjan Veljanoski, Prutha Chawda, Swen Gaudl, C. Oliver Hanemann, Emmanuel Ifeachor

2025

Abstract

Accurate preoperative prediction of meningioma grade is crucial for enhancing the clinical management of these tumours. In this study, we developed a non-invasive machine learning (ML) model to predict meningioma grade using clinical features and radiomics features from preoperative MRI scans, focusing on interpretability to improve clinical adoption of such models. A dataset of 94 patients from The Cancer Imaging Archive (TCIA) was analysed. Clinical features and radiomics features from T1-weighted contrast-enhanced (T1C) and T2-weighted Fluid Attenuated Inversion Recovery (T2 FLAIR) scans were utilised. Two feature subsets were constructed: one using radiomics features alone and the other combining clinical and radiomics features. Feature selection was performed using a modified Least Absolute Shrinkage and Selection Operator (LASSO) technique. Four ML models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB), were developed. SHapley Additive exPlanations (SHAP) was employed to address the blackbox nature of ML models by providing radiomics overall feature importance scores and model interpretation. Results using the clinical-radiomics subset showed that the SVM outperformed others (test AUC: 0.83), indicating its reliability for predicting meningioma grade. SHAP highlights discriminative radiomics features and their interaction with clinical features, thereby enhancing the clinical adoption of such models.

Paper Citation

in Harvard Style

Golbaf A., Veljanoski D., Chawda P., Gaudl S., Hanemann C. and Ifeachor E. (2025). An Interpretable Machine Learning Model for Meningioma Grade Prediction. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 65-74. DOI: 10.5220/0013114000003911

in Bibtex Style

@conference{healthinf25,
author={Ali Golbaf and Damjan Veljanoski and Prutha Chawda and Swen Gaudl and C. Hanemann and Emmanuel Ifeachor},
title={An Interpretable Machine Learning Model for Meningioma Grade Prediction},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={65-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013114000003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - An Interpretable Machine Learning Model for Meningioma Grade Prediction
SN - 978-989-758-731-3
AU - Golbaf A.
AU - Veljanoski D.
AU - Chawda P.
AU - Gaudl S.
AU - Hanemann C.
AU - Ifeachor E.
PY - 2025
SP - 65
EP - 74
DO - 10.5220/0013114000003911
PB - SciTePress