Diagnostic value of radiomics in glioblastoma: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

GAO Ziyang ^# , XIAO Yuan ^# , ZHU Fei , ZHANG Wenjing ,  LUI Su

Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

LUI Su, Email: lusuwcums@hotmail.com

Keywords：

Radiomics; Diagnosis; Glioblastoma; Meta-analysis; Systematic review

DOI：

10.7507/1672-2531.202108134

Video：

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Objective To systematically review the value of radiomics in the diagnosis of glioblastoma. Methods PubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect studies on radiomics in the grading of gliomas or the differentiation diagnosis from inception to May 30^th, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias and the quality of the included studies. Meta-analysis was then performed using Meta-Disc 1.4 software and RevMan 5.3 software. Results A total of 37 studies involving 2 746 subjects were included. The results of meta-analysis showed that the pooled sensitivity, specificity, and diagnostic odds ratio for the diagnosis of glioblastoma by radiomics were 0.91 (95%CI 0.89 to 0.92), 0.88 (95%CI 0.87 to 0.90), and 78.00 (95%CI 50.81 to 119.72), respectively. The area under the summary receiver operating characteristic (SROC) curve was 0.95. The key radiomic features for correct diagnosis of glioblastoma included intensity features and texture features of the lesions. Conclusion The current evidence shows that radiomics provides good diagnostic accuracy for glioblastoma. Due to the limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.

Citation： GAO Ziyang, XIAO Yuan, ZHU Fei, ZHANG Wenjing, LUI Su. Diagnostic value of radiomics in glioblastoma: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2022, 22(2): 232-242. doi: 10.7507/1672-2531.202108134 Copy

1.	Ostrom QT, Gittleman H, Truitt G, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Neuro Oncol, 2018, 20(suppl4): iv1-iv86.
2.	Tan AC, Ashley DM, López GY, et al. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin, 2020, 70(4): 299-312.
3.	Jung BC, Arevalo-Perez J, Lyo JK, et al. Comparison of glioblastomas and brain metastases using dynamic contrast-enhanced perfusion MRI. J Neuroimaging, 2016, 26(2): 240-246.
4.	Savage J, Quint D. Atypical imaging findings in an immunocompetent patient. Primary central nervous system lymphoma. JAMA Oncol, 2015, 1(2): 247-248.
5.	Upadhyay N, Waldman AD. Conventional MRI evaluation of gliomas. Br J Radiol, 2011, 84(2): S107-111.
6.	张晓琦, 李永丽, 窦社伟, 等. 动态对比增强MRI在胶质母细胞瘤与脑转移瘤鉴别诊断中的应用. 中华放射学杂志, 2015, (6): 410-413.
7.	朱亮飞, 田国忠, 刘广义, 等. 磁共振波谱成像结合扩散加权成像在脑胶质瘤及脑转移瘤鉴别诊断中的意义. 现代生物医学进展, 2015, 15(19): 3731-3733,3739.
8.	Durmo F, Rydelius A, Cuellar Baena S, et al. Multivoxel 1 H-MR spectroscopy biometrics for preoprerative differentiation between brain tumors. Tomography, 2018, 4(4): 172-181.
9.	You SH, Yun TJ, Choi HJ, et al. Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging. Eur Radiol, 2018, 28(9): 3801-3810.
10.	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. Sci Rep, 2020, 10(1): 12110.
11.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
12.	Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics, 2019, 9(5): 1303-1322.
13.	Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
14.	Fan Y, Chen C, Zhao F, et al. Radiomics-based machine learning technology enables better differentiation between glioblastoma and anaplastic oligodendroglioma. Front Oncol, 2019, 9: 1164.
15.	Qian Z, Li Y, Wang Y, et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett, 2019, 451: 128-135.
16.	Suh HB, Choi YS, Bae S, et al. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach. Eur Radiol, 2018, 28(9): 3832-3839.
17.	Yun J, Park JE, Lee H, et al. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Sci Rep, 2019, 9(1): 5746.
18.	Mühlbauer J, Egen L, Kowalewski KF, et al. Radiomics in renal cell carcinoma-a systematic review and meta-analysis. Cancers (Basel), 2021, 13(6): 1348.
19.	Ursprung S, Beer L, Bruining A, et al. Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol, 2020, 30(6): 3558-3566.
20.	Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
21.	Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging, 2019, 50(2): 519-528.
22.	Bao S, Watanabe Y, Takahashi H, et al. Differentiating between glioblastoma and primary CNS lymphoma using combined whole-tumor histogram analysis of the normalized cerebral blood volume and the apparent diffusion coefficient. Magn Reson Med Sci, 2019, 18(1): 53-61.
23.	Bathla G, Priya S, Liu Y, et al. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur Radiol, 2021, 31(11): 8703-8713.
24.	Chen C, Ou X, Wang J, et al. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors. Front Oncol, 2019, 9: 806.
25.	Chen C, Zheng A, Ou X, et al. Comparison of radiomics-based machine-learning classifiers in diagnosis of glioblastoma from primary central nervous system lymphoma. Front Oncol, 2020, 10: 1151.
26.	Chen Y, Li Z, Wu G, et al. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int J Neurosci, 2018, 128(7): 608-618.
27.	Choi YS, Lee HJ, Ahn SS, et al. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient. Eur Radiol, 2017, 27(4): 1344-1351.
28.	Dong F, Li Q, Jiang B, et al. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers. Eur Radiol, 2020, 30(5): 3015-3022.
29.	Dong F, Li Q, Xu D, et al. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol, 2019, 29(8): 3968-3975.
30.	Kang D, Park JE, Kim YH, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol, 2018, 20(9): 1251-1261.
31.	Kim Y, Cho HH, Kim ST, et al. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology, 2018, 60(12): 1297-1305.
32.	Kong Z, Jiang C, Zhu R, et al. 18 F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma. Neuroimage Clin, 2019, 23: 101912.
33.	Lin X, Lee M, Buck O, et al. Diagnostic accuracy of t1-weighted dynamic contrast-enhanced-MRI and DWI-ADC for differentiation of glioblastoma and primary CNS lymphoma. AJNR Am J Neuroradiol, 2017, 38(3): 485-491.
34.	Ma JH, Kim HS, Rim NJ, et al. Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions. AJNR Am J Neuroradiol, 2010, 31(9): 1699-1706.
35.	Mehrnahad M, Rostami S, Kimia F, et al. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients. Neuroradiol J, 2020, 33(5): 428-436.
36.	Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, et al. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med, 2020, 76: 44-54.
37.	Petrujkić K, Milošević N, Rajković N, et al. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol, 2019, 119: 108634.
38.	Priya S, Liu Y, Ward C, et al. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep, 2021, 11(1): 10478.
39.	Priya S, Ward C, Locke T, et al. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study. Neuroradiol J, 2021, 34(4): 320-328.
40.	Qin J, Li Y, Liang D, et al. Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis. Medicine (Baltimore), 2019, 98(42): e17515.
41.	Sha Z, Song Y, Wu Y, et al. The value of texture analysis in peritumoral edema of differentiating diagnosis between glioblastoma and primary brain lymphoma. Br J Neurosurg, 2020, (11): 1-4.
42.	Shrot S, Salhov M, Dvorski N, et al. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology, 2019, 61(7): 757-765.
43.	Skogen K, Schulz A, Helseth E, et al. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol, 2019, 60(3): 356-366.
44.	Svolos P, Tsolaki E, Kapsalaki E, et al. Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging, 2013, 31(9): 1567-1577.
45.	Tian Z, Chen C, Fan Y, et al. Glioblastoma and anaplastic astrocytoma: differentiation using MRI texture analysis. Front Oncol, 2019, 9: 876.
46.	Xiao DD, Yan PF, Wang YX, et al. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis. Clin Neurol Neurosurg, 2018, 173: 84-90.
47.	Zhang G, Chen X, Zhang S, et al. Discrimination between solitary brain metastasis and glioblastoma multiforme by using ADC-based texture analysis: a comparison of two different ROI placements. Acad Radiol, 2019, 26(11): 1466-1472.
48.	Guo J, Ren J, Shen J, et al. Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients. Diagn Interv Radiol, 2021, 27(3): 440-449.
49.	Hsieh KL, Lo CM, Hsiao CJ. Computer-aided grading of gliomas based on local and global MRI features. Comput Methods Programs Biomed, 2017, 139: 31-38.
50.	Lo CM, Chen YC, Weng RC, et al. Intelligent glioma grading based on deep transfer learning of mri radiomic features. Appl Sci Basel, 2019, 9(22): 4926.
51.	Tian Q, Yan LF, Zhang X, et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging, 2018, 48(6): 1518-1528.
52.	Wiestler B, Kluge A, Lukas M, et al. Multiparametric MRI-based differentiation of WHO gradeⅡ/Ⅲ glioma and WHO grade IV glioblastoma. Sci Rep, 2016, 6: 35142.
53.	Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
54.	Larue RT, Defraene G, De Ruysscher D, et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol, 2017, 90(1070): 20160665.
55.	Lu X, Xu W, Wei Y, et al. Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis. Neurol Sci, 2019, 40(5): 947-956.
56.	Giraud P, Giraud P, Gasnier A, et al. Radiomics and machine learning for radiotherapy in head and neck cancers. Front Oncol, 2019, 9: 174.
57.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
58.	Chaibub Neto E, Pratap A, Perumal TM, et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. NPJ Digit Med, 2019, 2: 99.
59.	Chaddad A, Kucharczyk MJ, Daniel P, et al. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol, 2019, 9: 374.
60.	Hu LS, Ning S, Eschbacher JM, et al. Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One, 2015, 10(11): e0141506.
61.	Gaudino S, Martucci M, Russo R, et al. MR imaging of brain pilocytic astrocytoma: beyond the stereotype of benign astrocytoma. Childs Nerv Syst, 2017, 33(1): 35-54.
62.	Hu LS, Hawkins-Daarud A, Wang L, et al. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett, 2020, 477: 97-106.
63.	Guzmán-De-Villoria JA, Mateos-Pérez JM, Fernández-García P, et al. Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors. Cancer Imaging, 2014, 14(1): 35.
64.	Louis DN, Perry A, Reifenberger G, et al. The 2016 World health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol, 2016, 131(6): 803-820.
65.	Kuo MD, Jamshidi N. Behind the numbers: Decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology, 2014, 270(2): 320-325.
66.	Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol, 2017, 19(1): 128-137.
67.	Zwanenburg A, Leger S, Vallières M, et al. Initiative for the IBS. Image biomarker standardisation initiative. Available at: https://www.arxivorg/abs/161207003.2018.

1. Ostrom QT, Gittleman H, Truitt G, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Neuro Oncol, 2018, 20(suppl4): iv1-iv86.
2. Tan AC, Ashley DM, López GY, et al. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin, 2020, 70(4): 299-312.
3. Jung BC, Arevalo-Perez J, Lyo JK, et al. Comparison of glioblastomas and brain metastases using dynamic contrast-enhanced perfusion MRI. J Neuroimaging, 2016, 26(2): 240-246.
4. Savage J, Quint D. Atypical imaging findings in an immunocompetent patient. Primary central nervous system lymphoma. JAMA Oncol, 2015, 1(2): 247-248.
5. Upadhyay N, Waldman AD. Conventional MRI evaluation of gliomas. Br J Radiol, 2011, 84(2): S107-111.
6. 张晓琦, 李永丽, 窦社伟, 等. 动态对比增强MRI在胶质母细胞瘤与脑转移瘤鉴别诊断中的应用. 中华放射学杂志, 2015, (6): 410-413.
7. 朱亮飞, 田国忠, 刘广义, 等. 磁共振波谱成像结合扩散加权成像在脑胶质瘤及脑转移瘤鉴别诊断中的意义. 现代生物医学进展, 2015, 15(19): 3731-3733,3739.
8. Durmo F, Rydelius A, Cuellar Baena S, et al. Multivoxel 1 H-MR spectroscopy biometrics for preoprerative differentiation between brain tumors. Tomography, 2018, 4(4): 172-181.
9. You SH, Yun TJ, Choi HJ, et al. Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging. Eur Radiol, 2018, 28(9): 3801-3810.
10. 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. Sci Rep, 2020, 10(1): 12110.
11. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
12. Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics, 2019, 9(5): 1303-1322.
13. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
14. Fan Y, Chen C, Zhao F, et al. Radiomics-based machine learning technology enables better differentiation between glioblastoma and anaplastic oligodendroglioma. Front Oncol, 2019, 9: 1164.
15. Qian Z, Li Y, Wang Y, et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett, 2019, 451: 128-135.
16. Suh HB, Choi YS, Bae S, et al. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach. Eur Radiol, 2018, 28(9): 3832-3839.
17. Yun J, Park JE, Lee H, et al. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Sci Rep, 2019, 9(1): 5746.
18. Mühlbauer J, Egen L, Kowalewski KF, et al. Radiomics in renal cell carcinoma-a systematic review and meta-analysis. Cancers (Basel), 2021, 13(6): 1348.
19. Ursprung S, Beer L, Bruining A, et al. Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol, 2020, 30(6): 3558-3566.
20. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
21. Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging, 2019, 50(2): 519-528.
22. Bao S, Watanabe Y, Takahashi H, et al. Differentiating between glioblastoma and primary CNS lymphoma using combined whole-tumor histogram analysis of the normalized cerebral blood volume and the apparent diffusion coefficient. Magn Reson Med Sci, 2019, 18(1): 53-61.
23. Bathla G, Priya S, Liu Y, et al. Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques. Eur Radiol, 2021, 31(11): 8703-8713.
24. Chen C, Ou X, Wang J, et al. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors. Front Oncol, 2019, 9: 806.
25. Chen C, Zheng A, Ou X, et al. Comparison of radiomics-based machine-learning classifiers in diagnosis of glioblastoma from primary central nervous system lymphoma. Front Oncol, 2020, 10: 1151.
26. Chen Y, Li Z, Wu G, et al. Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features. Int J Neurosci, 2018, 128(7): 608-618.
27. Choi YS, Lee HJ, Ahn SS, et al. Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient. Eur Radiol, 2017, 27(4): 1344-1351.
28. Dong F, Li Q, Jiang B, et al. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers. Eur Radiol, 2020, 30(5): 3015-3022.
29. Dong F, Li Q, Xu D, et al. Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. Eur Radiol, 2019, 29(8): 3968-3975.
30. Kang D, Park JE, Kim YH, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol, 2018, 20(9): 1251-1261.
31. Kim Y, Cho HH, Kim ST, et al. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI. Neuroradiology, 2018, 60(12): 1297-1305.
32. Kong Z, Jiang C, Zhu R, et al. 18 F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma. Neuroimage Clin, 2019, 23: 101912.
33. Lin X, Lee M, Buck O, et al. Diagnostic accuracy of t1-weighted dynamic contrast-enhanced-MRI and DWI-ADC for differentiation of glioblastoma and primary CNS lymphoma. AJNR Am J Neuroradiol, 2017, 38(3): 485-491.
34. Ma JH, Kim HS, Rim NJ, et al. Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions. AJNR Am J Neuroradiol, 2010, 31(9): 1699-1706.
35. Mehrnahad M, Rostami S, Kimia F, et al. Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients. Neuroradiol J, 2020, 33(5): 428-436.
36. Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, et al. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med, 2020, 76: 44-54.
37. Petrujkić K, Milošević N, Rajković N, et al. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol, 2019, 119: 108634.
38. Priya S, Liu Y, Ward C, et al. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep, 2021, 11(1): 10478.
39. Priya S, Ward C, Locke T, et al. Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis - a machine learning study. Neuroradiol J, 2021, 34(4): 320-328.
40. Qin J, Li Y, Liang D, et al. Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis. Medicine (Baltimore), 2019, 98(42): e17515.
41. Sha Z, Song Y, Wu Y, et al. The value of texture analysis in peritumoral edema of differentiating diagnosis between glioblastoma and primary brain lymphoma. Br J Neurosurg, 2020, (11): 1-4.
42. Shrot S, Salhov M, Dvorski N, et al. Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology, 2019, 61(7): 757-765.
43. Skogen K, Schulz A, Helseth E, et al. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol, 2019, 60(3): 356-366.
44. Svolos P, Tsolaki E, Kapsalaki E, et al. Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging, 2013, 31(9): 1567-1577.
45. Tian Z, Chen C, Fan Y, et al. Glioblastoma and anaplastic astrocytoma: differentiation using MRI texture analysis. Front Oncol, 2019, 9: 876.
46. Xiao DD, Yan PF, Wang YX, et al. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis. Clin Neurol Neurosurg, 2018, 173: 84-90.
47. Zhang G, Chen X, Zhang S, et al. Discrimination between solitary brain metastasis and glioblastoma multiforme by using ADC-based texture analysis: a comparison of two different ROI placements. Acad Radiol, 2019, 26(11): 1466-1472.
48. Guo J, Ren J, Shen J, et al. Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients. Diagn Interv Radiol, 2021, 27(3): 440-449.
49. Hsieh KL, Lo CM, Hsiao CJ. Computer-aided grading of gliomas based on local and global MRI features. Comput Methods Programs Biomed, 2017, 139: 31-38.
50. Lo CM, Chen YC, Weng RC, et al. Intelligent glioma grading based on deep transfer learning of mri radiomic features. Appl Sci Basel, 2019, 9(22): 4926.
51. Tian Q, Yan LF, Zhang X, et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging, 2018, 48(6): 1518-1528.
52. Wiestler B, Kluge A, Lukas M, et al. Multiparametric MRI-based differentiation of WHO gradeⅡ/Ⅲ glioma and WHO grade IV glioblastoma. Sci Rep, 2016, 6: 35142.
53. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
54. Larue RT, Defraene G, De Ruysscher D, et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol, 2017, 90(1070): 20160665.
55. Lu X, Xu W, Wei Y, et al. Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis. Neurol Sci, 2019, 40(5): 947-956.
56. Giraud P, Giraud P, Gasnier A, et al. Radiomics and machine learning for radiotherapy in head and neck cancers. Front Oncol, 2019, 9: 174.
57. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
58. Chaibub Neto E, Pratap A, Perumal TM, et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. NPJ Digit Med, 2019, 2: 99.
59. Chaddad A, Kucharczyk MJ, Daniel P, et al. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol, 2019, 9: 374.
60. Hu LS, Ning S, Eschbacher JM, et al. Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One, 2015, 10(11): e0141506.
61. Gaudino S, Martucci M, Russo R, et al. MR imaging of brain pilocytic astrocytoma: beyond the stereotype of benign astrocytoma. Childs Nerv Syst, 2017, 33(1): 35-54.
62. Hu LS, Hawkins-Daarud A, Wang L, et al. Imaging of intratumoral heterogeneity in high-grade glioma. Cancer Lett, 2020, 477: 97-106.
63. Guzmán-De-Villoria JA, Mateos-Pérez JM, Fernández-García P, et al. Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors. Cancer Imaging, 2014, 14(1): 35.
64. Louis DN, Perry A, Reifenberger G, et al. The 2016 World health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol, 2016, 131(6): 803-820.
65. Kuo MD, Jamshidi N. Behind the numbers: Decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology, 2014, 270(2): 320-325.
66. Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol, 2017, 19(1): 128-137.
67. Zwanenburg A, Leger S, Vallières M, et al. Initiative for the IBS. Image biomarker standardisation initiative. Available at: https://www.arxivorg/abs/161207003.2018.

Chinese Journal of Evidence-Based Medicine

Diagnostic value of radiomics in glioblastoma: a meta-analysis

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