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Clinical application progress of MRI-based radiomics in gliomas
OU Yanghong  LIU Guangyao  BAI Yuping  HAN Nan  ZHANG Jing 

Cite this article as: Ou YH, Liu GY, Bai YP, et al. Clinical application progress of MRI-based radiomics in gliomas. Chin J Magn Reson Imaging, 2020, 11(1): 74-76. DOI:10.12015/issn.1674-8034.2020.01.017.


[Abstract] Radiomics is a continuously evolving, noninvasive radiomics technique to quantify macroscopic tissue heterogeneity indirectly linked to microscopic tissue heterogeneity beyond human visual perception. In recent years, research on radiomics has been increasing and is widely used in oncology. Moreover, the extracted image features can also be studied in combination with genomics, proteomics, metabolomics. With the development of computer technology, it could potentially develop into a valuable clinical tool in routine oncologic imaging. In this paper, the basic concept of radiomics and various clinical applications of MRI-based radiomics in gliomas are reviewed in recent years.
[Keywords] gliomas；magnetic resonance imaging

OU Yanghong Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China

LIU Guangyao Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China

BAI Yuping Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China

HAN Nan Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China

ZHANG Jing* Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China

*Corresponding to: Zhang J, E-mail: lztong2001@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by the Higher Education of Gansu Province No. 2018A-124
Received  2019-07-16
Accepted  2019-11-21
DOI: 10.12015/issn.1674-8034.2020.01.017
Cite this article as: Ou YH, Liu GY, Bai YP, et al. Clinical application progress of MRI-based radiomics in gliomas. Chin J Magn Reson Imaging, 2020, 11(1): 74-76. DOI:10.12015/issn.1674-8034.2020.01.017.

[1]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2015, 278(2): 563-577.
[2]
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015, Cancer J Clin, 2016, 66(2): 115-132.
[3]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta neuropathologica, 2016, 131(6): 803-820.
[4]
Ostrom QT, Gittleman H, Fulop J, et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro Oncology, 2015, 17(suppl 4): iv1-iv62.
[5]
Gutman DA, Cooper LAD, Hwang SN, et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology, 2013, 267(2): 560-569.
[6]
Gulsen S. Achieving higher diagnostic results in stereotactic brain biopsy by simple and novel technique. Open Access Maced J Med Sci, 2015, 3(1): 99-104.
[7]
Yu J, Shi Z, Lian Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol, 2017, 27(8): 3509-3522.
[8]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
[9]
Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
[10]
王紫君，陈星池，黄劲柏，等．影像组学结合临床对非小细胞肺癌基因状态的预测.临床放射学杂志, 2019，44(6): 1033-1037.
[11]
Park HJ, Lee SS, Park B, et al. Radiomics analysis of gadoxetic acid-enhanced MRI for staging liver fibrosis. Radiology, 2018, 290(2): 380-387.
[12]
Guo D, Gu D, Wang H, et al. Radiomics analysis enables recurrence prediction for hepatocellular carcinoma after liver transplantation. Eur J Radiol, 2019, 117: 33-40.
[13]
Qin J, Liu Z, Zhang H, et al. Grading of gliomas by using radiomic features on multiple magnetic resonance imaging (MRI) sequences. Med Sci Monit, 2017, 23: 2168-2178.
[14]
Su C, Jiang J, Zhang S, et al. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol, 2019, 29(4): 1986-1996.
[15]
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.
[16]
Fetit AE, Novak J, Rodriguez D, et al. Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis. NMR Biomed, 2018, 31(1): e3781.
[17]
Xu R, Kido S, Suga K, et al. Texture analysis on (18) F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med, 2014, 28(9): 926-935.
[18]
Han Y, Zhen X, Zang Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neuro Oncol, 2018, 140(2): 297-306.
[19]
Li ZC, Bai H, Sun Q, et al. Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol, 2018, 28(9): 3640-3650.
[20]
Chang P, Grinband J, Weinberg BD, et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. Am J Neuroradiol, 2018, 39(7): 1201-1207.
[21]
Bahrami N, Piccioni D, Karunamuni R, et al. Edge contrast of the FLAIR hyperintense region predicts survival in patients with high-grade gliomas following treatment with bevacizumab. Am J Neuroradiol, 2018, 39(6): 1017-1024.
[22]
Yang D, Rao G, Martinez J, et al. Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys, 2015, 42(11): 6725-6735.
[23]
Chang K, Zhang B, Guo X, et al. Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol, 2016, 18(12): 1680-1687.
[24]
Xi Y, Guo F, Xu Z, et al. Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging, 2018, 47(5): 1380-1387.
[25]
Brynolfsson P, Nilsson D, Henriksson R, et al. ADC texture-An imaging biomarker for high-grade glioma? Med Phys, 2014, 41(10): 101903.
[26]
Sanghani P, Ang BT, King NKK, et al. Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning. Surg Oncol, 2018, 27(4): 709-714.
[27]
Macyszyn L, Akbari H, Pisapia JM, et al. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol, 2015, 18(3): 417-425.
[28]
Nasseri M, Gahramanov S, Netto JP, et al. Evaluation of pseudoprogression in patients with glioblastoma multiforme using dynamic magnetic resonance imaging with ferumoxytol calls RANO criteria into question. Neuro Oncol, 2014, 16(8): 1146-1154.
[29]
Hu X, Wong KK, Young GS, et al. Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging, 2011, 33(2): 296-305.
[30]
Jang BS, Jeon SH, Kim I H, et al. Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci Rep, 2018, 8(1): 12516.
[31]
Lotan E, Jain R, Razavian N, et al. State of the art: machine learning applications in glioma imaging. Am J Roentgenol, 2019, 212(1): 26-37.
[32]
陈绪珠，马军．影像组学在脑胶质瘤中的研究进展．磁共振成像, 2018, 9(10): 721-724.
[33]
Ge W. A perspective on deep imaging. IEEE Access, 2017, 4(99): 8914-8924.

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