Share:
Share this content in WeChat
X
Clinical Article
The value of magnetic resonance imaging in differentiating grade Ⅱ solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma
FU Shengli  REN Yande  LI Xiangrong  MA Chi  ZHANG Hua  GE Yaqiong 

Cite this article as: Fu SL, Ren YD, Li XR, et al. The value of magnetic resonance imaging in differentiating grade Ⅱ solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma[J]. Chin J Magn Reson Imaging, 2022, 13(1): 15-20. DOI:10.12015/issn.1674-8034.2022.01.004.


[Abstract] Objective To investigate the value of radiomics features with multi-parameter MRI images in differential diagnosis between intracranial grade Ⅱ solitary fibrous tumor/hemangiopericytoma (SFT/HPC) and angiomatous meningioma (AM).Materials and Methods: A total of 68 patients with grade Ⅱ SFT/HPC and 41 patients with AM confirmed by surgery or pathology were retrospectively analyzed from the First Affiliated Hospital of Qingdao University and Guangxi Medical University, all of the patients were performed T1WI, FLAIR and contrasted TIWI scan. The patients were randomly divided into training set (n=77) and validation set (n=32) in a ratio of 7∶3. After a normalization approach applied on the image, the region of interest (ROI) along the tumor edge step by step based on the axial image with 3D slicer software were sketched, then the radiomics features were extracted in the ROI with 3D slicer software. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied to reduce the dimension, then the radiomics features with the most diagnostic value were selected to build a binary Logistic regression model. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the model.Results 16, 13 and 12 radiomics features were extracted from T1WI, FLAIR and contrasted T1WI scan, respectively; additional 9 radiomics features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.98 (sensitivity 100%, specificity 92.86%) for T1WI model, 0.92 (73.47%, 100%) for FLAIR model, 0.89 (79.59%, 85.19%) for contrasted T1WI model, and 0.99 (98.04%, 96.15%) for the combined sequence model and were enough to correctly distinguish the two groups in 87.50%、75.00%、68.75% and 90.63% of cases in test set, respectively.Conclusions The differentiation efficiency of multi-parameter MRI images radiomics features between intracranial grade Ⅱ SFT/HPC and AM was better than single sequence. T1WI was the highest diagnosis efficacy sequence among single sequence.
[Keywords] magnetic resonance imaging;hemangiopericytoma;angiomatous meningioma;radiomics;differentiation performance

FU Shengli1   REN Yande1*   LI Xiangrong2   MA Chi1   ZHANG Hua1   GE Yaqiong3  

1 Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266555, China

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

3 GE HealthCare China (Shanghai), Shanghai 210000, China

Ren YD, E-mail: 8198458@163.com

Conflicts of interest   None.

Received  2021-08-22
Accepted  2021-12-29
DOI: 10.12015/issn.1674-8034.2022.01.004
Cite this article as: Fu SL, Ren YD, Li XR, et al. The value of magnetic resonance imaging in differentiating grade Ⅱ solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma[J]. Chin J Magn Reson Imaging, 2022, 13(1): 15-20.DOI:10.12015/issn.1674-8034.2022.01.004

[1]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary[J]. Acta Neuropathol, 2016, 131(6): 803-820. DOI: 10.1007/s00401-016-1545-1.
[2]
Shankar JJS, Hodgson L, Sinha N.Diffusion weighted imaging may help differentiate intracranial hemangiopericytoma from meningioma[J]. J Neuroradiol, 2019, 46(4): 263-267. DOI: 10.1016/j.neurad.2018.11.002.
[3]
Hua L, Luan S, Li H, et al. Angiomatous Meningiomas Have a Very Benign Outcome Despite Frequent Peritumoral Edema at Onset[J]. World Neurosurg, 2017, 108: 465-473. DOI: 10.1016/j.wneu.2017.08.096.
[4]
He L, Li B, Song X, et al. Signal value difference between white matter and tumor parenchyma in T1- and T2- weighted images may help differentiating solitary fibrous tumor/hemangiopericytoma and angiomatous meningioma[J]. Clin Neurol Neurosurg, 2020, 198: 106221. DOI: 10.1016/j.clineuro.2020.106221.
[5]
Wei J, Li L, Han Y, et al. Accurate Preoperative Distinction of Intracranial Hemangiopericytoma From Meningioma Using a Multihabitat and Multisequence-Based Radiomics Diagnostic Technique[J]. Front Oncol, 2020, 10: 534. DOI: 10.3389/fonc.2020.00534.
[6]
El-Ali AM, Agarwal V, Thomas A, et al. Clinical metric for differentiating intracranial hemangiopericytomas from meningiomas using diffusion weighted MRI[J]. Clin Imaging, 2019, 54: 1-5. DOI: 10.1016/j.clinimag.2018.10.018.
[7]
Liang XH, Zhou Q, Zhao ZY, et al. The value of DWI combined with minimum ADC value in differential diagnosis of intracranial solitary fibrous tumor/hemangiopericytoma and meningioma. Chin J Magn Reson Imaging, 2019, 10(1): 8-13. DOI: 10.12015/issn.1674-8034.2019.01.002.
[8]
Wei G, Kang X, Liu X, et al. Intracranial meningeal hemangiopericytoma: Recurrences at the initial and distant intracranial sites and extraneural metastases to multiple organs[J]. Mol Clin Oncol, 2015, 3(4): 770-774. DOI: 10.3892/mco.2015.537.
[9]
Yip CM, Hsu SS, Liao WC, et al. Intracranial solitary fibrous tumor/hemangiopericytoma - A case series[J]. Surg Neurol Int, 2020,11: 414. DOI: 10.25259/SNI_490_2020.
[10]
Chen LF, Yang Y, Yu XG, et al. Multimodal treatment and management strategies for intracranial hemangiopericytoma[J]. J Clin Neurosci, 2015, 22(4): 718-25. DOI: 10.1016/j.jocn.2014.11.011.
[11]
Park YW, Choi YS, Ahn SS, et al. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors[J]. Korean J Radiol, 2019, 20(9): 1381-1389. DOI: 10.3348/kjr.2018.0814.
[12]
Gore S, Chougule T, Jagtap J, et al. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization[J]. Acad Radiol, 2021, 28(11): 1599-1621. DOI: 10.1016/j.acra.2020.06.016.
[13]
Zhu Y, Man C, Gong L, et al. A deep learning radiomics model for preoperative grading in meningioma[J]. Eur J Radiol, 2019, 116: 128-134. DOI: 10.1016/j.ejrad.2019.04.022.
[14]
Bae S, An C, Ahn SS, et al. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation[J]. Sci Rep, 2020, 10(1): 12110. DOI: 10.1038/s41598-020-68980-6.
[15]
Wang J, Liu X, Hu B, et al. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy[J]. Abdom Radiol (NY), 2021, 46(5): 1805-1815. DOI: 10.1007/s00261-020-02846-3.
[16]
Chen HM, Liu J, Cheng ZX, et al. Value of radiomics nomogram based on T1WI for pretreatment prediction of relapse within 1 year in osteosarcoma: a multicenter study[J]. Chin J Radiol, 2020, 54(9): 874-881. DOI: 10.3760/cma.j.cn112149-20200512-00675.
[17]
Dong J, Yu M, Miao Y, et al. Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model[J]. Biomed Res Int, 2020, 2020: 5042356. DOI: 10.1155/2020/5042356.
[18]
Li X, Lu Y, Xiong J, et al. Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis[J]. J Neuroradiol, 2019, 46(5): 281-287. DOI: 10.1016/j.neurad.2019.05.013.
[19]
Xia W, Hu B, Li HQ, et al. Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model[J]. J Magn Reson Imaging, 2021, 54(3): 880-887. DOI: 10.1002/jmri.27592.
[20]
Zhao YJ, Lu YP, Li XX, et al. The Evaluation of Radiomic Models in Distinguishing Pilocytic Astrocytoma From Cystic Oligodendroglioma With Multiparametric MRI[J]. J Comput Assist Tomogr, 2020, 44(6): 969-976. DOI: 10.1097/RCT.0000000000001088.
[21]
Chen C, Ren CP. Value of Apparent Diffusion Coefficient (ADC) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentially Diagnosing Angiomatous Meningiomas and Solitary Fibrous Tumors/Hemangiopericytomas[J]. Med Sci Monit, 2019, 25: 5992-5996. DOI: 10.12659/MSM.915308.
[22]
He L, Li BH, Song XY, et al. Signal value difference between white matter and tumor parenchyma in T1- and T2- weighted images may help differentiating solitary fibrous tumor/ hemangiopericytoma and angiomatous meningioma[J]. Clin Neurol Neurosurg, 2020, 198: 106221. DOI: 10.1016/j.clineuro.2020.106221.
[23]
Kandemirli SG, Chopra S, Priya S, et al. Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging[J]. Clin Neurol Neurosurg, 2020, 198: 106205. DOI: 10.1016/j.clineuro.2020.106205.
[24]
Vickers AJ, Van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis[J]. Diagn Progn Res, 2019, 3: 18. DOI: 10.1186/s41512-019-0064-7.

PREV Three-dimensional arterial spin labeling perfusion imaging shows cerebral blood flow decline in some brain regions in preschool autistic children
NEXT A preliminary study on the efficacy of tumor necrosis factor alpha antagonists in the treatment of axial spondyloarthropathy by T1-mapping technique
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn