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• Clinical Article •
Preoperatively predict pathological grading of meningiomas using radiomics model based on transverse and sagittal enhanced T1WI images: a preliminary study
YANG Chunxue  YUAN Meng  ZHANG Jinling  WANG Tianzuo 

Cite this article as: Yang CX, Yuan M, Zhang JL, et al. Preoperatively predict pathological grading of meningiomas using radiomics model based on transverse and sagittal enhanced T1WI images: a preliminary study[J]. Chin J Magn Reson Imaging, 2022, 13(2): 6-9. DOI:10.12015/issn.1674-8034.2022.02.002.


[Abstract] Objective To develop a radiomics model based on transverse and sagittal enhanced T1WI images for preoperatively predicting pathological grading of meningiomas, and test its performance.Materials and Methods A total of 132 patients with pathologically confirmed meningiomas from January 2017 to December 2020 were enrolled according to the inclusion criteria. ITK-SNAP was used to draw regions of interest, and then features were extracted using pyradiomics. According to the ratio of 8∶2, 105 patients were used as the training set and 27 patients were selected as the test set. Feature reproducibility was evaluated using intra-class correlation coefficient, and the models were developed using support vector machine with RBF kernel after feature selection. Finally, the test set was used to assess the performance, and receiver operating characteristic (ROC) curves were plotted.Results The combined models based on transverse and sagittal images outperformed other models using single sequence, and synthetic minority over sampling technique (SMOTE) could improve the performance to some degree. The combined model using SMOTE demonstrated the best performance, and the area under the curve, sensitivity, specificity and accuracy were 0.982, 0.900, 1.000 and 0.963 in the test set, respectively.Conclusions The radiomics model based on transverse and sagittal enhanced T1WI images can help to preoperatively predict pathological grading of meningiomas.
[Keywords] magnetic resonance imaging；radiomics；meningioma；machine learning；pathological grading

YANG Chunxue1   YUAN Meng1   ZHANG Jinling1*   WANG Tianzuo2*  

1 CT Room, the Second Affiliated Hospital of Harbin Medical University, Harbin 150000, China

2 Department of Radiology, the Sixth Affiliated Hospital of Harbin Medical University, Harbin 150000, China

Zhang JL, E-mail: zhangjinling@hrbmu.edu.cn Wang TZ, E-mail: agntwz@126.com

Conflicts of interest   None.

Received  2021-08-24
Accepted  2021-12-28
DOI: 10.12015/issn.1674-8034.2022.02.002
Cite this article as: Yang CX, Yuan M, Zhang JL, et al. Preoperatively predict pathological grading of meningiomas using radiomics model based on transverse and sagittal enhanced T1WI images: a preliminary study[J]. Chin J Magn Reson Imaging, 2022, 13(2): 6-9. DOI:10.12015/issn.1674-8034.2022.02.002.

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