AUTHOR=Luo Ming , Lin Guihan , Chen Duoning , Chen Weiyue , Xia Shuiwei , Hui Junguo , Chen Pengjun , Chen Minjiang , Ye Wangyang , Ji Jiansong 

TITLE=MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1554539

DOI=10.3389/fneur.2025.1554539

ISSN=1664-2295

ABSTRACT=BackgroundHigh expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to develop a predictive model for the Ki-67 index in meningioma based on preoperative magnetic resonance imaging (MRI).MethodsThis study included 196 patients from one center (internal cohort) and 92 patients from another center (external validation cohort). Meningioma had to have been pathologically confirmed for inclusion. The Ki-67 index was classified as high (Ki-67 ≥ 5%) and low (Ki-67 < 5%). The internal cohort was randomly assigned to training and validation sets at a 7:3 ratio. Radiomics features were selected from contrast-enhanced T1-weighted MRI using the least-absolute shrinkage and selection operator and random forest methods. Then, we constructed a predictive model based on the identified semantic and radiomics features, aiming to distinguish high and low Ki-67 expression. The model’s performance was evaluated through internal cross-validation and validated in the external cohort.ResultsAmong the clinical features, peritumoral edema (p = 0.001) and heterogeneous enhancement (p = 0.001) were independent predictors of the Ki-67 index in meningiomas. The radiomics model using a combined 8 mm volume of interest demonstrated optimal performance in the training (area under the receiver operating characteristic curve [AUC] = 0.883) and validation (AUC = 0.811) sets. A nomogram integrating clinical and radiomic features was constructed, achieving an AUC of 0.904 and enhancing the model’s predictive accuracy for high Ki-67 expression.ConclusionThis study developed clinical-radiomic models to non-invasively predict Ki-67 expression in meningioma and provided a novel preoperative strategy for assessing tumor proliferation.