Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

Simple Summary A high Ki-67 index usually suggests accelerated and uncontrolled cell proliferation correlated with tumor growth and is a prognostic factor that is associated with an increased recurrent risk in meningioma patients. The aim of our study is to predict the Ki-67 proliferative index in meningioma patients using machine learning technology. With 371 cases collected from two centers, we systematically analyzed the relevance between clinical/radiological features and the Ki-67 index. Moreover, with radiomic features extracted from postcontrast images, we built three radiomic models and three clinical radiological-radiomic models to predict the Ki-67 status. The models showed good performance, with an AUC of 0.837 in the internal test and 0.700 in the external test. The results provide a quantitative method to facilitate clinical decision making for meningioma patients. Background/aim This study aimed to explore the value of radiological and radiomic features retrieved from magnetic resonance imaging in the prediction of a Ki-67 proliferative index in meningioma patients using a machine learning model. Methods This multicenter, retrospective study included 371 patients collected from two centers. The Ki-67 expression was classified into low-expressed and high-expressed groups with a threshold of 5%. Clinical features and radiological features were collected and analyzed by using univariate and multivariate statistical analyses. Radiomic features were extracted from contrast-enhanced images, followed by three independent feature selections. Six predictive models were constructed with different combinations of features by using linear discriminant analysis (LDA) classifier. Results The multivariate analysis suggested that the presence of intratumoral necrosis (p = 0.032) and maximum diameter (p < 0.001) were independently correlated with a high Ki-67 status. The predictive models showed good performance with AUC of 0.837, accuracy of 0.810, sensitivity of 0.857, and specificity of 0.771 in the internal test and with AUC of 0.700, accuracy of 0.557, sensitivity of 0.314, and specificity of 0.885 in the external test. Conclusion The results of this study suggest that the predictive model can efficiently predict the Ki-67 index of meningioma patients to facilitate the therapeutic management.

Automated summary

The study aimed to predict the Ki-67 proliferative index in meningioma patients using machine learning technology. By analyzing clinical/radiological features, and building radiomic models, it provided a quantitative method for clinical decision making.