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Clinical Article
Machine learning models based on radiomics in differentiating solitary fibrous tumor from angiomatous meningioma
BI Yuzhen  BAI Jie  BAI Peirui  LI Xiangrong  FU Shengli  WANG Jian  REN Yande 

BI Y Z, BAI J, BAI P R, et al. Machine learning models based on radiomics in differentiating solitary fibrous tumor from angiomatous meningioma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 50-55. DOI:10.12015/issn.1674-8034.2023.09.009.

[Abstract] Objective To investigate the value of machine learning models based on MRI radiomics features in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM).Materials and Methods A total of 68 patients with SFT and 41 patients with AM confirmed by pathology from the Affiliated Hospital of Qingdao University and the First Affiliated Hospital of Guangxi Medical University were retrospectively enrolled. The pre-processing, delineation of the region of interest (ROI), and feature extraction of the T1-weighted images (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1WI were performed in the 3D slicer software. The optimal feature set was selected by independent-samples t test and least absolute shrinkage and selection operator (LASSO). The optimal features of multi-parameter MRI were selected based on T1WI, FLAIR and contrast-enhanced T1WI. All patients were randomly divided into the training group (n=76) and the test group (n=33) at a ratio of 7∶3. The models were established by logistic regression (LR), random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) curves were drawn, respectively, and accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The DeLong test was used to compare the differences in AUCs among different models.Results The average age of the AM group was higher than that of the SFT group (P<0.001). There was no significant difference in gender composition between AM group and SFT group (P>0.05). Twenty-two, twelve, twelve and sixty-five radiomics features were extracted from T1WI, FLAIR, contrast-enhanced T1WI and multi-parameter MRI, respectively. The differentiation efficiency of models based on multi-parameter MRI between intracranial SFT and AM was better than that of models based on a single sequence. The SVM model based on multi-parameter MRI reached the highest performance of all models, and the AUC was 0.99. Among models based on a single sequence, differentiation efficiency of models based on T1WI or FLAIR was better than that of models based on contrast-enhanced T1WI. The AUCs of LR models were all greater than 0.9.Conclusions Machine learning models based on MRI radiomics features can effectively discriminate SFT from AM. The differentiation efficiency of models based on multi-parameter MRI is higher, and the SVM model has the highest efficiency among these models. LR models have good efficiency and stability.
[Keywords] solitary fibrous tumor;angiomatous meningioma;magnetic resonance imaging;radiomics;machine learning

BI Yuzhen1   BAI Jie2   BAI Peirui3   LI Xiangrong4   FU Shengli1   WANG Jian3   REN Yande1*  

1 Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China

2 Department of Magnetic Resonance, the Frist Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China

3 College of Electronic Information Engineering, Shandong University of Science and Technology, Qingdao 266555, China

4 Department of Radiology, the Frist Affiliated Hospital of Guangxi Medical University, Nanning 530000, China

Corresponding author: Ren YD, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Qingdao Medical and Health Scientific Research Project (No. 2021-WJZD192); Science and Technology Project of Shinan District of Qingdao (No. 2022-4-010-YY).
Received  2023-03-22
Accepted  2023-08-04
DOI: 10.12015/issn.1674-8034.2023.09.009
BI Y Z, BAI J, BAI P R, et al. Machine learning models based on radiomics in differentiating solitary fibrous tumor from angiomatous meningioma[J]. Chin J Magn Reson Imaging, 2023, 14(9): 50-55. DOI:10.12015/issn.1674-8034.2023.09.009.

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