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• Clinical Article •
Study on value of intra-tumoral and peri-tumoral features of multimodal MRI radiomics in distinguishing fibrous from nonfibrous meningiomas
YANG Huimin  LI Wenxin  JIANG Xingyue  WANG Qianqian  ZHANG Juntao  LIU Xinjiang 

DOI:10.12015/issn.1674-8034.2025.08.008.


[Abstract] Objective To investigate the clinical value of T2WI-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI) of the tumour body and peritumour in combination with conventional factors in identifyingfibrous and non-fibrous meningiomas.Materials and Methods A total of 108 patients with pathologically confirmed meningiomas, including 30 fibrous meningiomas and 78 non-fibrous meningiomas, were enrolled and divided into a training set (n = 76) and a validation set (n = 32) in a ratio of 7 : 3. In the training set, 1132 radiomics features were extracted from the tumour body and peri-tumour of T2WI and CE-T1WI sequences, respectively. The optimal subset of radiomics features was identified through the maximal correlation minimal redundancy method (mRMR) and the least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) machine learning method to construct imaging genomics models: T2WI tumour, T2WI peritumour, CE-T1WI tumour, CE-T1WI peritumour, (T2WI+CE-T1WI) tumour, (T2WI+CE-T1WI) peritumour and (T2WI+CE-T1WI) tumour+peritumour. The conventional factors with significance (P < 0.05) were screened by single-factor and multifactor logistic regression analysis methods. Then, the radiomics model with the best discriminatory efficacy was combined with the conventional factors to generate nomograms, and the diagnostic efficacy of the nomograms was evaluated by AUC, and the clinical efficacy of the model was assessed by the net benefit value of the decision curve analysis (DCA). the efficacy of this model was validated in the test set.Results The AUC values for the T2WI tumour, T2WI peritumour, CE-T1WI tumour, CE-T1WI peritumour, (T2WI+CE-T1WI) tumour, (T2WI+CE-T1WI) peritumour and (T2WI+CE-T1WI) tumour+peritumour radiomics models in the training set were 0.925, 0.803, 0.837, 0.872, 0.902, 0.894, 0.908, respectively. In the test set, the corresponding values were 0.652, 0.812, 0.700, 0.725, 0.700, 0.816, 0.729. The AUC of the T2WI tumour radiomics model for identifying fibrous and non-fibrous meningiomas was 0.92 in the training set and 0.65 in the test set. This appeared to be an overfitting. The (T2WI+CE-T1WI) peritumour radiomics model had the highest AUC value in the test set, and the model demonstrated the best diagnostic efficacy. The discriminatory efficacy of the established (T2WI+CE-T1WI) peri-tumour radiomics model was improved from the combined model with conventional factors (T2WI signal intensity and peri-tumour oedema), and its AUCs in the training set and test set were 0.89 and 0.82, respectively. The calibration curves showed good agreement between the predicted and actual probabilities of the model's preoperative identification of fibrous and non-fibrous meningiomas, DCA results show good clinical efficacy for this model.Conclusions Multimodal MRI radiomics models can effectively identify fibrous and non-fibrous meningiomas, and their discriminatory efficacy can be futher improved when combined with conventional factors.
[Keywords] meningioma；radiomics；typing；peri-tumour；magnetic resonance imaging

YANG Huimin1   LI Wenxin1   JIANG Xingyue2   WANG Qianqian2   ZHANG Juntao3   LIU Xinjiang1*  

1 Department of Radiology, Shanghai Pudong Hospital (Pudong Hospital of Fudan University), Shanghai 201399, China

2 Department of Radiology, the Affiliated Hospital of Binzhou Medical College, Binzhou 256603, China

3 GE Healthcare PDX GMS medical affairs, Shanghai 200203, China

Corresponding author: LIU X J, E-mail: lxj6513@163.com

Conflicts of interest   None.

Received  2024-12-30
Accepted  2025-07-04
DOI: 10.12015/issn.1674-8034.2025.08.008
DOI:10.12015/issn.1674-8034.2025.08.008.

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