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Clinical Article
Differentiation of high-grade glioma and solitary brain metastasis based on radiomics features fusion of multiparametric MRI
XU Xiangdong  LIANG Fangrong  WEI Ruili  WU Jialiang  ZHANG Wanli  WANG Linjing  YANG Ruimeng  ZHEN Xin  LAI Shengsheng 

Cite this article as: Xu XD, Liang FR, Wei RL, et al. Differentiation of high-grade glioma and solitary brain metastasis based on radiomics features fusion of multiparametric MRI sequences[J]. Chin J Magn Reson Imaging, 2022, 13(11): 53-59, 65. DOI:10.12015/issn.1674-8034.2022.11.010.


[Abstract] Objective To explore the value of a new prediction model based on the fusion of multiparametric MRI imaging features in the differential diagnosis of high-grade glioma (HGG) and solitary brain metastasis (SBM).Materials and Methods We collected multiparametric MRI images of 121 (61 HGG and 60 SBM) patients in this study, and delineated the tumor volume of solid enhancement region (VOIET) on the conventional axial MRI images [T1WI, T2WI, T2-weighted fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI)]. The radiomics features extracted from different MRI sequences were fused by merging the class information of HGG and SBM, and the performance of different MRI sequences and their combinations were compared quantitatively.Results Fusion of image features extracted from the T1WI and T2_FLAIR sequences had dominant predictive performances over features from other single sequence or combinations, achieving a discrimination accuracy of area under the ROC curve (AUC), accuracy, sensitivity and specificity of 0.946, 86.4%, 84.1% and 88.7%, respectively.Conclusions The fusion model based on radiomics features from multiparameter MRI could noninvasively and efficiently identify HGG and SBM via integrating multi-sequence image information of the tumor.
[Keywords] brain tumor;high-grade glioma;solitary brain metastasis;radiomics;magnetic resonance imaging;differential diagnosis

XU Xiangdong1   LIANG Fangrong1, 2   WEI Ruili1, 2   WU Jialiang3   ZHANG Wanli1, 2   WANG Linjing4   YANG Ruimeng1, 2   ZHEN Xin5   LAI Shengsheng6*  

1 Department of Radiology, Guangzhou First People's Hospital, Guangzhou 510180, China

2 School of Medicine, South China University of Technology, Guangzhou 510006, China

3 Department of Radiology, the University of Hong Kong Shenzhen hospital, Shenzhen 518000, China

4 Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China

5 School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China

6 School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou 510520, China

Lai SS, E-mail: laiss@gdyzy.edu.cn

Conflicts of interest   None.

Received  2022-06-27
Accepted  2022-11-06
DOI: 10.12015/issn.1674-8034.2022.11.010
Cite this article as: Xu XD, Liang FR, Wei RL, et al. Differentiation of high-grade glioma and solitary brain metastasis based on radiomics features fusion of multiparametric MRI sequences[J]. Chin J Magn Reson Imaging, 2022, 13(11): 53-59, 65.DOI:10.12015/issn.1674-8034.2022.11.010

[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]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[3]
Hanif F, Muzaffar K, Perveen K, et al. Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment[J]. Asian Pac J Cancer Prev, 2017, 18(1): 3-9. DOI: 10.22034/APJCP.2017.18.1.3.
[4]
Gritsch S, Batchelor TT, Gonzalez Castro LN. Diagnostic, therapeutic, and prognostic implications of the 2021 World Health Organization classification of tumors of the central nervous system[J]. Cancer, 2022, 128(1): 47-58. DOI: 10.1002/cncr.33918.
[5]
Meier R, Pahud de Mortanges A, Wiest R, et al. Exploratory analysis of qualitative MR imaging features for the differentiation of glioblastoma and brain metastases[J/OL]. Front Oncol, 2020, 10: 581037 [2022-06-26]. https://doi.org/10.3389/fonc.2020.581037. DOI: 10.3389/fonc.2020.581037.
[6]
Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, et al. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach[J]. Phys Med, 2020, 76: 44-54. DOI: 10.1016/j.ejmp.2020.06.016.
[7]
Abdel Razek AAK, Talaat M, El-Serougy L, et al. Differentiating glioblastomas from solitary brain metastases using arterial spin labeling perfusion- and diffusion tensor imaging-derived metrics[J/OL]. World Neurosurg, 2019, 127: e593-e598 [2022-06-27]. https://doi.org/10.1016/j.wneu.2019.03.213. DOI: 10.1016/j.wneu.2019.03.213.
[8]
Ye ZZ, Price RL, Liu XR, et al. Diffusion histology imaging combining diffusion basis spectrum imaging (DBSI) and machine learning improves detection and classification of glioblastoma pathology[J]. Clin Cancer Res, 2020, 26(20): 5388-5399. DOI: 10.1158/1078-0432.CCR-20-0736.
[9]
Mao JJ, Zeng WK, Zhang QY, et al. Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models[J/OL]. BMC Med Imaging, 2020, 20(1): 124 [2022-06-26]. https://doi.org/10.1186/s12880-020-00524-w. DOI: 10.1186/s12880-020-00524-w.
[10]
Jung BC, Arevalo-Perez J, Lyo JK, et al. Comparison of glioblastomas and brain metastases using dynamic contrast-enhanced perfusion MRI[J]. J Neuroimaging, 2016, 26(2): 240-246. DOI: 10.1111/jon.12281.
[11]
Law M, Cha S, Knopp EA, et al. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging[J]. Radiology, 2002, 222(3): 715-721. DOI: 10.1148/radiol.2223010558.
[12]
Zhang XM, Ruan SJ, Xiao WB, et al. Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: a two-center study[J/OL]. Clin Transl Med, 2020, 10(2): e111 [2022-06-26]. https://doi.org/10.1002/ctm2.111. DOI: 10.1002/ctm2.111.
[13]
Lotan E, Jain R, Razavian N, et al. State of the art: machine learning applications in glioma imaging[J]. AJR Am J Roentgenol, 2019, 212(1): 26-37. DOI: 10.2214/AJR.18.20218.
[14]
Skogen K, Schulz A, Helseth E, et al. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis[J]. Acta Radiol, 2019, 60(3): 356-366. DOI: 10.1177/0284185118780889.
[15]
Swinburne NC, Schefflein J, Yu SK, et al. Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging[J/OL]. Ann Transl Med, 2019, 7(11): 232 [2022-06-26]. http://dx.doi.org/10.21037/atm.2018.08.05. DOI: 10.21037/atm.2018.08.05.
[16]
Tateishi M, Nakaura T, Kitajima M, et al. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases[J/OL]. J Neurol Sci, 2020, 410: 116514 [2022-06-26]. https://doi.org/10.1016/j.jns.2019.116514. DOI: 10.1016/j.jns.2019.116514.
[17]
Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis[J]. J Magn Reson Imaging, 2019, 50(2): 519-528. DOI: 10.1002/jmri.26643.
[18]
Chen CY, Ou XJ, Wang J, et al. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors[J/OL]. Front Oncol, 2019, 9: 806 [2022-06-26]. https://doi.org/10.3389/fonc.2019.00806. DOI: 10.3389/fonc.2019.00806.
[19]
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/OL]. Sci Rep, 2020, 10(1): 12110 [2022-06-26]. https://doi.org/10.1038/s41598-020-68980-6. DOI: 10.1038/s41598-020-68980-6.
[20]
van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype[J/OL]. Cancer Res, 2017, 77(21): e104-e107 [2022-06-26]. https://doi.org/10.1158/0008-5472.CAN-17-0339. DOI: 10.1158/0008-5472.CAN-17-0339.
[21]
Haghighat M, Abdel-Mottaleb M, Alhalabi W. Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition[J]. IEEE Trans Inf Forensics Secur, 2016, 11(9): 1984-1996. DOI: 10.1109/TIFS.2016.2569061.
[22]
Brown G, Pocock A, Zhao M J, et al. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection[J]. J Mach Learn Res, 2012, 13: 27-66. DOI: 10.1080/00207179.2012.669851.
[23]
Li J, Cheng K, Wang S, et al. Feature selection: A data perspective[J]. ACM Comput Surv, 2017, 50(6): 1-45. DOI: 10.1145/3136625.
[24]
Janjua TI, Rewatkar P, Ahmed-Cox A, et al. Frontiers in the treatment of glioblastoma: Past, present and emerging[J]. Adv Drug Deliv Rev, 2021, 171: 108-138. DOI: 10.1016/j.addr.2021.01.012.
[25]
Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma[J]. J Hepatol, 2019, 70(6): 1133-1144. DOI: 10.1016/j.jhep.2019.02.023.
[26]
Petrujkić K, Milošević N, Rajković N, et al. Computational quantitative MR image features-a potential useful tool in differentiating glioblastoma from solitary brain metastasis[J/OL]. Eur J Radiol, 2019, 119: 108634 [2022-06-26]. https://doi.org/10.1016/j.ejrad.2019.08.003. DOI: 10.1016/j.ejrad.2019.08.003.
[27]
Tozer DJ, Jäger HR, Danchaivijitr N, et al. Apparent diffusion coefficient histograms may predict low-grade glioma subtype[J]. NMR Biomed, 2007, 20(1): 49-57. DOI: 10.1002/nbm.1091.
[28]
Ayala-Domínguez L, Pérez-Cárdenas E, Avilés-Salas A, et al. Quantitative imaging parameters of contrast-enhanced micro-computed tomography correlate with angiogenesis and necrosis in a subcutaneous C6 glioma model[J/OL]. Cancers (Basel), 2020, 12(11): 3417 [2022-06-26]. https://doi.org/10.3390/cancers12113417. DOI: 10.3390/cancers12113417.
[29]
Zhang Q, Guo YX, Zhang WL, et al. Intra-tumoral angiogenesis correlates with immune features and prognosis in glioma[J]. Aging (Albany NY), 2022, 14(10): 4402-4424. DOI: 10.18632/aging.204079.
[30]
Voicu IP, Pravatà E, Panara V, et al. Differentiating solitary brain metastases from high-grade gliomas with MR: comparing qualitative versus quantitative diagnostic strategies[J]. Radiol Med, 2022, 127(8): 891-898. DOI: 10.1007/s11547-022-01516-2.
[31]
Bumes E, Fellner C, Fellner FA, et al. Validation study for non-invasive prediction of IDH mutation status in patients with glioma using in vivo 1H-magnetic resonance spectroscopy and machine learning[J/OL]. Cancers, 2022, 14(11): 2762 [2022-06-26]. https://doi.org/10.3390/cancers14112762.
[32]
Galijasevic M, Steiger R, Mangesius S, et al. Magnetic resonance spectroscopy in diagnosis and follow-up of gliomas: state-of-the-art[J/OL]. Cancers, 2022, 14(13): 3197 [2022-06-26]. https://doi.org/10.3390/cancers14133197. DOI: 10.3390/cancers14133197.

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