Related Experiment Video
Updated: Jan 9, 2026
Translational Orthotopic Models of Glioblastoma Multiforme
Published on: February 17, 2023
Brain-tumor interface-based MRI radiomics models to predict Ki-67 proliferation status of meningiomas: A multi-center
Ping Gao1, Jiaxin Wei1, Wei Wang2
1The Fourth Hospital of Harbin Medical University, China.
Objective:
To evaluate the potential of brain-tumor interface (BTI) radiomics for predicting the Ki-67 proliferation index in meningiomas and to construct and validate a nomogram integrating radiomics with clinical features for this purpose.
Materials And Methods:
This multicenter retrospective study enrolled 300 patients from two distinct centers. Patients diagnosed with meningioma were stratified into low (<5%) and high (≥5%) Ki-67 expression groups based on immunohistochemistry. Clinical data were collected, and independent predictors were identified via univariate and multivariate logistic regression analyses. Radiomics features from the tumor parenchyma and the 4 mm BTI region were extracted from T1-CE and T2WI images to construct single-region and combined-region radiomics models. A nomogram integrating the radiomics scores (Radscore) from the optimal radiomics model and clinical predictors was developed and validated.
Results:
In the internal validation cohort, the tumor radiomics model achieved an AUC of 0.754, the BTI radiomics model achieved an AUC of 0.702, and the combined Radiomics model performed better with an AUC of 0.780, significantly outperforming both single-region models.In the external test cohort, corresponding AUCs were 0.681 for the tumor radiomics model, 0.682 for the BTI radiomics model, and 0.709 for the combined Radiomics model, again showing the combined model's superiority. Univariate and multivariate analyses identified tumor volume and maximum diameter as independent clinical predictors of Ki-67 proliferation status. The clinical model incorporating these features reached an AUC of 0.741 in the internal validation cohort and 0.688 in the external test cohort. The nomogram integrating radiomics scores with clinical predictors achieved the highest diagnostic performance (AUC of 0.846 in the internal validation cohort, 0.752 in external test cohort), significantly outperforming all other evaluated models.
Conclusions:
The BTI region shows the potential for predicting meningioma Ki-67 proliferation. The nomogram developed in this study provides an effective tool for this prediction, supporting personalized treatment strategies.
