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. 2022 Jul 26;14(15):3637.
doi: 10.3390/cancers14153637.

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

Yanjie Zhao  1   2 , Jianfeng Xu  3 , Boran Chen  1 , Le Cao  4 , Chaoyue Chen  1   2
Affiliations

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

Yanjie Zhao et al. Cancers (Basel). .

Abstract

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.

Keywords: Ki-67; machine learning; magnetic resonance imaging (MRI); meningioma; radiomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of patient selection.
Figure 2
Figure 2
Immunohistochemical staining for Ki-67 in meningiomas.
Figure 3
Figure 3
The workflow for the development of machine learning models to predict the Ki-67 index in meningioma patients. ETC: Extra tree classifier; Lasso: Least absolute shrinkage and selection operator; LDA: Linear discriminant analysis; SVC: Support vector machine; ROIs: Region of interests; AUC: Area under curve.
Figure 4
Figure 4
Distribution of maximum tumor diameter and tumor volume of Ki-67 ≥ 5% and Ki-67 < 5% meningiomas.
Figure 5
Figure 5
Receiver operating characteristic curves of Lasso+ LDA machine learning models. (A): Internal test (n = 62) of radiomic model; (B): External test (n = 61) of radiomic model; (C): Internal test (n = 62) of clinical radiological–radiomic-based model; (D): External test (n = 61) of clinical radiological–radiomic-based model; AUC: Area under Curve.
See this image and copyright information in PMC

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