Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T2*-weighted radiomic-based extra trees models

Zhang, Mengze; Ouyang, Hanqiang; Liu, Jianfang; Jin, Dan; Wang, Chunjie; Ni, Ming; Liu, Xiaoguang; Lang, Ning; Jiang, Liang; Huishu, Yuan

doi:10.1007/s00330-021-08383-x

Cited by 8 publications

(1 citation statement)

References 44 publications

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“…The clinical radiological–radiomic-based models showed better performance in both the internal test and external test, with an AUC of 0.837 and 0.700, respectively. It has long been demonstrated in previous studies that some combined models may serve to outperform the single radiomic models or clinical models [ 31 , 32 , 33 ]. In contrast to the higher-order radiomic features, the relationship between these image parameters and tumor growth has long been established by researchers and provided a clearer interpretation of the models [ 25 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

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

Zhao

Chen

et al. 2022

Cancers

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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.

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