Share：
Share this content in WeChat
X
[Chinese][PDF]84746
• Reviews •
Research progress of deep learning and radiomics in glioma
LI Jie  LIU Guangyao  FAN Fengxian  HU Wanjun  BAI Yuping  ZHANG Jing 

Cite this article as: Li J, Liu GY, Fan FX, et al. Research progress of deep learning and radiomics in glioma[J]. Chin J Magn Reson Imaging, 2022, 13(4): 158-161. DOI:10.12015/issn.1674-8034.2022.04.035.


[Abstract] Deep learning is a branch of artificial intelligence. It has developed rapidly in disease detection and prognosis evaluation, and has become a popular research method, especially in the field of medical image in recent years. Radiomics is a very considerable method in the study of glioma. Deep learning and radiomics based on MRI can make differential diagnosis and classification of glioma, predict the genotype change status before operation, evaluate the treatment effect and predict the progression free survival and overall survival after operation, which provides a important basis for clinical treatment and postoperative follow-up. It is a research hotspot of glioma at present. This paper is to review the research progress of deep learning and radiomics based on MRI in the differential diagnosis, preoperative grading, genotyping and prognosis of glioma.
[Keywords] glioma；deep learning；radiomics；magnetic resonance imaging；differential diagnosis；preoperative classification；genotyping；survival prediction

LI Jie1, 2, 3   LIU Guangyao1, 2, 3   FAN Fengxian1, 3   HU Wanjun1, 3   BAI Yuping1, 2, 3   ZHANG Jing1, 3*  

1 Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China

2 Second Clinical School, Lanzhou University, Lanzhou 730030, China

3 Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China

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

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Gansu Province (No. 21JR1RA129); Science and Technology Project of Gansu Province (No. 21JR7RA438); Talent innovation and entrepreneurship project of Lanzhou Chengguan District (No. 2020RCCX0034).
Received  2021-12-31
Accepted  2022-04-02
DOI: 10.12015/issn.1674-8034.2022.04.035
Cite this article as: Li J, Liu GY, Fan FX, et al. Research progress of deep learning and radiomics in glioma[J]. Chin J Magn Reson Imaging, 2022, 13(4): 158-161. DOI:10.12015/issn.1674-8034.2022.04.035.

[1]
Wen J, Chen W, Zhu Y, et al. Clinical features associated with the efficacy of chemotherapy in patients with glioblastoma (GBM): a surveillance, epidemiology, and end results (SEER) analysis[J]. BMC Cancer, 2021, 21(1): 81. DOI: 10.1186/s12885-021-07800-0.
[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): 1-100. DOI: 10.1093/neuonc/noz150.
[3]
Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology[J]. J Intern Med, 2020, 288(1): 62-81. DOI: 10.1111/joim.13030.
[4]
Rudie JD, Rauschecker AM, Bryan RN, et al. Emerging Applications of Artificial Intelligence in Neuro-Oncology[J]. Radiology, 2019, 290(3): 607-618. DOI: 10.1148/radiol.2018181928.
[5]
Zegers C, Posch J, Traverso A, et al. Current applications of deep-learning in neuro-oncological MRI[J]. 2021, 83161-173. DOI: 10.1016/j.ejmp.2021.03.003.
[6]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[7]
Liu Z, Wang S, Dong D, et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges[J]. Theranostics, 2019, 9(5):1303-1322. DOI: 10.7150/thno.30309.
[8]
Abdel Razek AAK, Alksas A, Shehata M, et al. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging[J]. Insights Imaging, 2021, 12(1): 152. DOI: 10.1186/s13244-021-01102-6.
[9]
Li Z, Wang Y, Yu J, et al. Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma[J]. Sci Rep, 2017, 7(1): 5467. DOI: 10.1038/s41598-017-05848-2.
[10]
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.
[11]
Shen B, Zhang Z, Shi X, et al. Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks[J]. Eur J Nucl Med Mol Imaging, 2021, 48(11): 3482-3492. DOI: 10.1007/s00259-021-05326-y.
[12]
Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images[J]. Comput Biol Med, 2020, 121: 103758. DOI: 10.1016/j.compbiomed.2020.103758.
[13]
Buda M, AlBadawy EA, Saha A, et al. Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images[J]. Radiol Artif Intell, 2020, 2(1): e180050. DOI: 10.1148/ryai.2019180050.
[14]
Matsui Y, Maruyama T, Nitta M, et al. Prediction of lower-grade glioma molecular subtypes using deep learning[J]. J Neurooncol, 2020, 146(2): 321-327. DOI: 10.1007/s11060-019-03376-9.
[15]
Zhang Y, Liang K, He J, et al. Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion[J]. Front Oncol, 2021, 11: 665891. DOI: 10.3389/fonc.2021.665891.
[16]
Nakagawa M, Nakaura T, Namimoto T, et al. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma[J]. Eur J Radiol, 2018, 108: 147-154. DOI: 10.1016/j.ejrad.2018.09.017.
[17]
Wu W, Li J, Ye J, et al. Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning[J]. Front Oncol, 2021, 11: 639062. DOI: 10.3389/fonc.2021.639062.
[18]
Han Y, Yang Y, Shi ZS, et al. Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI[J]. Eur J Radiol, 2021, 134: 109467. DOI: 10.1016/j.ejrad.2020.109467.
[19]
Kobayashi K, Miyake M, Takahashi M, et al. Observing deep radiomics for the classification of glioma grades[J]. Sci Rep, 2021, 11(1): 10942. DOI: 10.1038/s41598-021-90555-2.
[20]
Mu JH, Zhang YW, Wu ZG. Application of different radiomics dignostic models based on conventional MR images in the preprotive grading of brain gliomag[J]. Chin J Magn Reson Imaging, 2020, 11(1): 55-59. DOI: 10.12015/issn.1674-8034.2020.01.012.
[21]
Tian Q, Yan LF, Zhang X, et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI[J]. J Magn Reson Imaging, 2018, 48(6): 1518-1528. DOI: 10.1002/jmri.26010.
[22]
Goryawala M, Roy B, Gupta RK, et al. T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading[J]. J Neuroimaging, 2021, 31(1): 124-131. DOI: 10.1111/jon.12800.
[23]
Yang Y, Yan LF, Zhang X, et al. Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning[J]. Front Neurosci, 2018, 12: 804. DOI: 10.3389/fnins.2018.00804.
[24]
Li Y, Wei D, Liu X, et al. Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning[J]. Eur Radiol, 2021. DOI: 10.1007/s00330-021-08237-6.
[25]
Gutta S, Acharya J, Shiroishi MS, et al. Improved Glioma Grading Using Deep Convolutional Neural Networks[J]. AJNR Am J Neuroradiol, 2021. 42(2): 233-239. DOI: 10.3174/ajnr.A6882.
[26]
Zhuge Y, Ning H, Mathen P, et al. Automated glioma grading on conventional MRI images using deep convolutional neural networks[J]. Med Phys, 2020, 47(7): 3044-3053. DOI: 10.1002/mp.14168.
[27]
Mzoughi H, Njeh I, Wali A, et al. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification[J]. J Digit Imaging, 2020, 33(4): 903-915. DOI: 10.1007/s10278-020-00347-9.
[28]
Zhang Z, Xiao J, Wu S, et al. Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades[J]. J Digit Imaging, 2020, 33(4): 826-837. DOI: 10.1007/s10278-020-00322-4.
[29]
Louis D, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary[J]. Neuro-Oncology, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[30]
Kanazawa T, Minami Y, Takahashi H, et al. Magnetic resonance imaging texture analyses in lower-grade gliomas with a commercially available software: correlation of apparent diffusion coefficient and T2 skewness with 1p/19q codeletion[J]. Neurosurgical Review, 2019, 43(4): 1211-1219. DOI: 10.1007/s10143-019-01157-6.
[31]
Pasquini L, Napolitano A, Tagliente E, et al. Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM[J]. J Pers Med, 2021, 11(4): 290. DOI: 10.3390/jpm11040290.
[32]
Yogananda C, Shah B, Yu F, et al. A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas[J]. Neuro-Oncology Advances, 2020, 2(Suppl 4): 42-48. DOI: 10.1093/noajnl/vdaa066.
[33]
Yogananda CGB, Shah BR, Nalawade SS, et al. MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status[J]. AJNR Am J Neuroradiol, 2021, 42(5): 845-852. DOI: 10.3174/ajnr.A7029.
[34]
Chang P, Grinband J, Weinberg BD, et al. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas[J]. AJNR Am J Neuroradiol, 2018, 39(7): 1201-1207. DOI: 10.3174/ajnr.A5667.
[35]
Choi KS, Choi SH and Jeong B. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network[J]. Neuro Oncol, 2019, 21(9): 1197-1209. DOI: 10.1093/neuonc/noz095.
[36]
Kim M, Jung SY, Park JE, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma[J]. Eur Radiol, 2020, 30(4): 2142-2151. DOI: 10.1007/s00330-019-06548-3.
[37]
Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, et al. A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas[J]. Neuro Oncol, 2020, 22(3): 402-411. DOI: 10.1093/neuonc/noz199.
[38]
Xue CQ, Du XH, Jin L, et al. Prediction of methylation status of MGMT promoter in WHO grade Ⅱ,Ⅲ glioma based on MRI deep learning model[J]. Chin J Radio, 2021, 55(7): 734-738. DOI: 10.3760/cma.j.cn112149-20200825-01029.
[39]
Gao Y, Xiao X, Han B, et al. Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation[J]. 2020, 8(11): e19805. DOI: 10.2196/19805.
[40]
Zhang Q, Cao J, Zhang J, et al. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images[J]. Comput Math Methods Med, 2019, 2019: 2893043. DOI: 10.1155/2019/2893043.
[41]
Lee J, Wang N, Turk S, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning[J]. Sci Rep, 2020, 10(1): 20331. DOI: 10.1038/s41598-020-77389-0.
[42]
Kim JY, Park JE, Jo Y, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients[J]. Neuro Oncol, 2019, 21(3): 404-414. DOI: 10.1093/neuonc/noy133.
[43]
Metz MC, Molina-Romero M, Lipkova J, et al. Predicting Glioblastoma Recurrence from Preoperative MR Scans Using Fractional-Anisotropy Maps with Free-Water Suppression[J]. Cancers (Basel), 2020, 12(3): 728. DOI: 10.3390/cancers12030728.
[44]
Sun L, Zhang S, Chen H, et al. Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning[J]. Front Neurosci, 2019, 13: 810. DOI: 10.3389/fnins.2019.00810.
[45]
Li G, Li L, Li Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas[J]. Brain, 2022. DOI: 10.1093/brain/awab340.
[46]
Feng X, Tustison NJ, Patel SH, et al. Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features[J]. Front Comput Neurosci, 2020, 14: 25. DOI: 10.3389/fncom.2020.00025.
[47]
Han W, Qin L, Bay C, et al. Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas[J]. AJNR Am J Neuroradiol, 2020, 41(1): 40-48. DOI: 10.3174/ajnr.A6365.
[48]
Zhang X, Lu H, Tian Q, et al. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival[J]. Eur Radiol, 2019, 29(10): 5528-5538. DOI: 10.1007/s00330-019-06069-z.
[49]
Huang H, Zhang W, Fang Y, et al. Overall Survival Prediction for Gliomas Using a Novel Compound Approach[J]. Front Oncol, 2021, 11: 724191. DOI: 10.3389/fonc.2021.724191.

PREV Research progress of rs-fMRI in brain ischemic white matter lesions
NEXT Research progress of multimodal functional magnetic resonance imaging in high intensive focused ultrasound ablation of uterus myomas
  


LINK


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