Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study.

RATIONALE AND

OBJECTIVES

This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).

MATERIALS AND

METHODS

The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier analysis.

RESULTS

Among the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The recurrence-free survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes.

CONCLUSION

The XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients.