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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

[1]
Wiemels J, Wrensch M, Claus EB. Epidemiology and etiology of meningioma[J]. J Neurooncol, 2010, 99(3): 307-314. DOI: 10.1007/s11060-010-0386-3.
[2]
Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016[J]. Neuro Oncol, 2019, 21(Suppl 5): v1-v100. DOI: 10.1093/neuonc/noz150.
[3]
Lam Shin Cheung V, Kim A, Sahgal A, et al. Meningioma recurrence rates following treatment: a systematic analysis[J]. J Neurooncol, 2018, 136(2): 351-361. DOI: 10.1007/s11060-017-2659-6.
[4]
Bertero L, Dalla Dea G, Osella-Abate S, et al. Prognostic Characterization of Higher-Grade Meningiomas: A Histopathological Score to Predict Progression and Outcome[J]. J Neuropathol Exp Neurol, 2019, 78(3): 248-256. DOI: 10.1093/jnen/nly127.
[5]
Wang YC, Chuang CC, Wei KC, et al. Long Term Surgical Outcome and Prognostic Factors of Atypical and Malignant Meningiomas[J]. Sci Rep, 2016, 6: 35743. DOI: 10.1038/srep35743.
[6]
Goldbrunner R, Minniti G, Preusser M, et al. EANO guidelines for the diagnosis and treatment of meningiomas[J]. Lancet Oncol, 2016, 17(9): e383-391. DOI: 10.1016/s1470-2045(16)30321-7.
[7]
Buerki RA, Horbinski CM, Kruser T, et al. An overview of meningiomas [J]. Future Oncol, 2018, 14(21): 2161-2177. DOI: 10.2217/fon-2018-0006.
[8]
Ke C, Chen H, Lv X, et al. Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI[J]. J Magn Reson Imaging, 2020, 51(6): 1810-1820. DOI: 10.1002/jmri.26976.
[9]
Qiu LH, Han FG, Tang GC, et al. The diagnostic value of conventional MRI and DWI for meningioma[J]. Radiol Prac, 2012, 27(5): 474-478. DOI: 10.3969/j.issn.1000-0313.2012.05.001.
[10]
Banzato T, Causin F, Della Puppa A, et al. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study[J]. J Magn Reson Imaging, 2019, 50(4): 1152-1159. DOI: 10.1002/jmri.26723.
[11]
Coroller TP, Bi WL, Huynh E, et al. Radiographic prediction of meningioma grade by semantic and radiomic features[J]. PLoS One, 2017, 12(11): e0187908. DOI: 10.1371/journal.pone.0187908.
[12]
Park YW, Oh J, You SC, et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging[J]. Eur Radiol, 2018, 29(8): 4068-4076. DOI: 10.1007/s00330-018-5830-3.
[13]
Huang RY, Bi WL, Griffith B, et al. Imaging and diagnostic advances for intracranial meningiomas[J]. Neuro Oncol, 2019, 21(Suppl 1): i44-i61. DOI: 10.1093/neuonc/noy143.
[14]
Han T, Zhou JL. Advances in imaging study on grading and typing of meningiomas [J]. Chin J Magn Reson Imaging, 2021, 12(7): 94-97. DOI: 10.12015/issn.1674-8034.2021.07.022.
[15]
Kawahara Y, Nakada M, Hayashi Y, et al. Prediction of high-grade meningioma by preoperative MRI assessment[J]. J Neurooncol, 2012, 108(1): 147-152. DOI: 10.1007/s11060-012-0809-4.
[16]
Bohara M, Nakajo M, Kamimura K, et al. Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters[J]. Acad Radiol, 2020, 27(3): 342-353. DOI: 10.1016/j.acra.2019.04.012.
[17]
Ugga L, Perillo T, Cuocolo R, et al. Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis[J]. Neuroradiology, 2021, 63(8): 1293-1304. DOI: 10.1007/s00234-021-02668-0.
[18]
Zheng F, Chen XZ. Status of artificial intelligence in meningioma image[J]. Chin J Magn Reson Imaging, 2020, 11(10): 934-936. DOI: 10.12015/issn.1674-8034.2020.10.025.
[19]
Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics[J]. Med Phys, 2020, 47(5): e185-e202. DOI: 10.1002/mp.13678.
[20]
Gu H, Zhang X, Di Russo P, et al. The Current State of Radiomics for Meningiomas: Promises and Challenges[J]. Front Oncol, 2020, 10: 567736. DOI: 10.3389/fonc.2020.567736.
[21]
Currie G, Hawk KE, Rohren E, et al. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging[J]. J Med Imaging Radiat Sci, 2019, 50(4): 477-487. DOI: 10.1016/j.jmir.2019.09.005.
[22]
Yan PF, Yan L, Hu TT, et al. The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation[J]. Transl Oncol, 2017, 10(4): 570-577. DOI: 10.1016/j.tranon.2017.04.006.
[23]
Laukamp KR, Shakirin G, Baessler B, et al. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading[J]. World Neurosurg, 2019, 132: e366-e390. DOI: 10.1016/j.wneu.2019.08.148.
[24]
Chu H, Lin X, He J, et al. Value of MRI Radiomics Based on Enhanced T1WI Images in Prediction of Meningiomas Grade[J]. Acad Radiol, 2020, 28(5): 687-693. DOI: 10.1016/j.acra.2020.03.034.

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