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Research progress of multimodal MRI radiomics and deep learning in glioma
WANG Ru  GAO Yang 

Cite this article as: WANG R, GAO Y. Research progress of multimodal MRI radiomics and deep learning in glioma[J]. Chin J Magn Reson Imaging, 2024, 15(7): 165-172. DOI:10.12015/issn.1674-8034.2024.07.028.


[Abstract] Diffuse gliomas are the most common primary malignant tumors of the brain, and preoperative precise grading and molecular typing prediction are crucial for developing appropriate treatment strategies and predicting survival rates. Imaging omics uses advanced feature analysis to extract data from medical images and construct predictive models to capture small changes in lesions, thereby improving the accuracy of clinical diagnosis, prognosis assessment, and treatment response prediction. Deep learning can automatically learn meaningful features for research, and can automatically learn and extract multi-layer features from a large amount of raw data, rather than manually made shallow features. As deep learning has been fully proven to accurately find very deep and abstract features, it has become a widely studied topic in the field of medical image analysis. With the advancement of computing power, deep learning based artificial intelligence has completely changed various fields. Promote the biological validation of radiomic features in gliomas. This study provides a review of the latest research on multimodal MRI radiomics and deep learning in preoperative grading, molecular typing, survival prediction, and treatment evaluation of glioma, with the aim of providing accurate diagnosis and treatment for glioma patients.
[Keywords] diffuse glioma；multimodal；magnetic resonance imaging；radiomics；deep learning；precise diagnosis and treatment

WANG Ru   GAO Yang*  

Department of Imaging Diagnostic, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China

Corresponding author: Gao Y, E-mail: 1390903990@qq.com

Conflicts of interest   None.

Received  2024-03-04
Accepted  2024-07-06
DOI: 10.12015/issn.1674-8034.2024.07.028
Cite this article as: WANG R, GAO Y. Research progress of multimodal MRI radiomics and deep learning in glioma[J]. Chin J Magn Reson Imaging, 2024, 15(7): 165-172. DOI:10.12015/issn.1674-8034.2024.07.028.

[1]
LOUIS D N, PERRY A, WESSELING P, et al. The 2021 WHO classification of tumors of the central nervous system:A summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[2]
BERA K, BRAMAN N, GUPTA A, et al. Predicting cancer outcomes with radiomics and artificial intelligence in radiology[J]. Nat Rev Clin Oncol, 2022, 19(2): 132-146. DOI: 10.1038/s41571-021-00560-7.
[3]
JI X L, DI Z, BING L. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme[J/OL]. J Transl Med, 2024, 22(1): 107 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38279111/. DOI: 10.1186/s12967-023-04823-y.
[4]
AFTAB K, AAMIR F B, MALLICK S, et al. Radiomics for precision medicine in glioblastoma[J]. J Neurooncol, 2022, 156(2): 217-231. DOI: 10.1007/s11060-021-03933-1.
[5]
YOGANANDA C G B, SHAH B R, NALAWADE S S, et al. MRI-based deep-learning method for determining glioma promoter methylation status[J]. AJNR Am J Neuroradiol, 2021, 42(5): 845-852. DOI: 10.3174/ajnr.A7029.
[6]
HAIDER A S, ENE C I, PALMISCIANO P, et al. Concurrent IDH1 and IDH2 mutations in glioblastoma: A case report[J/OL]. Front Oncol, 2023, 13: 1071792 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37077830/. DOI: 10.3389/fonc.2023.1071792.
[7]
NEUMAIER F, ZLATOPOLSKIY B D, NEUMAIER B. Mutated isocitrate dehydrogenase (mIDH) as target for PET imaging in gliomas[J/OL]. Molecules, 2023, 28(7): 2890[2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37049661/. DOI: 10.3390/molecules28072890.
[8]
LUO H, ZHUANG Q, WANG Y, et al. A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas[J]. Lab Invest, 2021, 101(4): 450-462. DOI: 10.1038/s41374-020-0472-x.
[9]
LIN K, CIDAN W J, QI Y, et al. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging[J]. Med Phys, 2022, 49(7): 4419-4429. DOI: 10.1002/mp.15648.
[10]
WANG P, XIE S H, WU Q, et al. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade[J]. Eur Radiol, 2023, 33(12): 8809-8820. DOI: 10.1007/s00330-023-09861-0.
[11]
ZHU F Y, SUNY F, YIN X P. Use of Radiomics models in preoperative grading of cerebral gliomas and comparison with three-dimensional arterial spin labelling[J]. Clin Oncol (R Coll Radiol), 2023, 35(11): 726-735. DOI: 10.1016/j.clon.2023.08.001.
[12]
SEBASTIAN R, FATIH I, MAARTEN M, et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning[J]. Neuro Oncol, 2023, 25(2): 279-289. DOI: 10.1093/neuonc/noac166.
[13]
SREEJITH V I, BUDHIRAJU V V, YOGEESWARI P, et al. Deep learning classifies low- and high-grade glioma patients with high accuracy, sensitivity, and specificity based on their brain white matter networks derived from diffusion tensor imaging[J/OL]. Diagnostics (Basel), 2022, 12(12): 3216 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/36553224/. DOI: 10.3390/diagnostics12123216.
[14]
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.
[15]
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.
[16]
KILLELA P J, PIROZZI C J, HEALY P. Mutations in IDH1, IDH2, and in the TERT promoter define clinically distinct subgroups of adult malignant gliomas[J]. Oncotarget, 2014, 5(6): 1515-1525. DOI: 10.18632/oncotarget.1765.
[17]
ŚLEDZIŃSKA P, BEBYN M G, FURTAK J, et al. Prognostic and predictive biomarkers in gliomas[J/OL]. Int J Mol Sci, 2021, 22(19): 10373 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34638714/. DOI: 10.3390/ijms221910373.
[18]
ZHANG H, OUYANG Y, ZHANG H, et al. Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas[J/OL]. Clin Radiol, 2024, 8: S0009-9260(24)00081-3[2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38402087/. DOI: 10.1016/j.crad.2024.01.030.
[19]
YUAN Z L, WEN J L, DONG B. The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery[J/OL]. Front Oncol, 2023, 13: 1265672 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38090497/. DOI: 10.3389/fonc.2023.1265672.
[20]
GIANFRANCO D S, LORENZO T, MARIA E L, et al. Accuracy of radiomics in predicting IDH mutation status in diffuse gliomas: A bivariate meta-analysis[J/OL]. Radiol Artif Intell, 2024, 6(1): e220257 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38231039/. DOI: 10.1148/ryai.220257.
[21]
IOANNIDIS G S, PIGOTT L E, IV M, et al. Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas[J/OL]. Front Neurol, 2023, 14: 1249452 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38046592/. DOI: 10.3389/fneur.2023.1249452.
[22]
LU J, XU W J, CHEN X C, et al. Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study[J]. Magn Reson Imaging, 2023, 104: 72-79. DOI: 10.1016/j.mri.2023.09.001.
[23]
WANG J, HU Y, ZHOU X J, et al. A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas[J/OL]. Eur J Radiol, 2022, 47: 110141 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34995947/. DOI: 10.1016/j.ejrad.2021.110141.
[24]
PASQUINI L, NAPOLITANO A, TAGLIENTE E, et al. Deep learning can differentiate IDH-mutant from IDH-wild GBM[J/OL]. J Pers Med, 2021, 11(4): 290 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/33918828/. DOI: 10.3390/jpm11040290.
[25]
LI Z J, WANG Y Y, YU J H, et al. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma[J/OL]. Sci Rep, 2017, 7(1):5467 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/28710497/. DOI: 10.1038/s41598-017-05848-2.
[26]
BANGALORE YOGANANDA C G, WAGNER B C, TRUONG N C D, et al. MRI-based deep learning method for classification of IDH mutation status[J/OL]. Bioengineering (Basel), 2023, 10(9): 1045 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37760146/. DOI: 10.3390/bioengineering10091045.
[27]
DONISELLI F M, PASCUZZO R, AGRÒ M, et al. Development of a radiomic model for MGMT promoter methylation detection in glioblastoma using conventional MRI[J/OL]. Int J Mol Sci, 2023, 25(1): 138 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38203308/. DOI: 10.3390/ijms25010138.
[28]
SAXENA S, JENA B, MOHAPATRA B, et al. Fused deep learning paradigm for the prediction of O6-methylguanine-DNA methyltransferase genotype in glioblastoma patients:A neuro-oncological investigation[J/OL]. Comput Biol Med, 2023, 153: 106492 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/36621191/. DOI: 10.1016/j.compbiomed.2022.106492.
[29]
LOUIS D N, PERRY A, WESSELING P, et al. Pre-operative MRI radiomics model non-invasively predicts key genomic markers and survival in glioblastoma patients[J]. J Neurooncol, 2022, 160(1): 253-263. DOI: 10.1007/s11060-022-04150-0.
[30]
CHEN S X, XU Y, YE M P, et al. Predicting MGMT promoter methylation in diffuse gliomas using deep learning with radiomics[J/OL]. J Clin Med, 2022, 11(12): 3445 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35743511/. DOI: 10.3390/jcm11123445.
[31]
HUANG L E. Impact of CDKN2A/B homozygous deletion on the prognosis and biology of IDH-mutant glioma[J/OL]. Biomedicine, 2022, 10(2): 246 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35203456/. DOI: 10.3390/biomedicines10020246.
[32]
LOUIS D, PERRY A, WESSELING P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary[J]. Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
[33]
YAE W P, KI S P, JI E P, et al. Qualitative and quantitative magnetic resonance imaging phenotypes may predict CDKN2A/B homozygous deletion status in isocitrate dehydrogenase-mutant astrocytomas: A multicenter study[J]. Korean J Radiol, 2023, 24(2): 133-144. DOI: 10.3348/kjr.2022.0732.
[34]
LI Q Z, RUI W, JUENI G. A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma[J]. Eur Radiol, 2024, 34(1): 391-399. DOI: 10.1007/s00330-023-09944-y.
[35]
GAO J N, LIU Z, PAN H Y, et al. Preoperative discrimination of CDKN2A/B homozygous deletion status in isocitrate dehydrogenase-mutant astrocytoma: A deep learning-based radiomics model using MRI[J]. J Magn Reson Imaging, 2024, 59(5): 1655-1664. DOI: 10.1002/jmri.28945.
[36]
GAO K, LI G, QU Y, et al. TERT promoter mutations and long telomere length predict poor survival and radiotherapy resistance in gliomas[J]. Oncotarget, 2016, 7(8): 8712-8725. DOI: 10.18632/oncotarget.6007.
[37]
LU J, LI X, LI H, et al. A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas[J/OL]. Clin Radiol, 2022, 77(8): e560-e567 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35595562/. DOI: 10.1016/j.crad.2022.04.005.
[38]
HUO X L, WANG Y L, MA S H, et al. Multimodal MRI-based radiomic nomogram for predicting telomerase reverse transcriptase promoter mutation in IDH-wildtype histological lower-grade gliomas[J/OL]. Medicine (Baltimore), 2023, 102(51): e36581 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38134061/. DOI: 10.1097/MD.0000000000036581.
[39]
LI Z C, KAISER L, HOLZGREVE A, et al. Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [(18)F]FET PET radiomics[J]. Eur J Nucl Med Mol Imaging, 2021, 48(13): 4415-4425. DOI: 10.1007/s00259-021-05526-6.
[40]
YAN J, ZHANG B, ZHANG S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients[J/OL]. NPJ Precis Oncol, 2021, 5(1): 72 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34312469/. DOI: 10.1038/s41698-021-00205-z.
[41]
ZHANG H B, ZHOU B B, ZHANG H W, et al. Peritumoral radiomics for identification of telomerase reverse transcriptase promoter mutation in patients with glioblastoma based on preoperative MRI[J]. Can Assoc Radiol J, 2024, 75(1): 143-152. DOI: 10.1177/08465371231183309.
[42]
BUZ-YALUG B, TURHAN G, CETIN A I, et al. Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas[J/OL].Eur J Radiol, 2024, 170: 111257 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38134710/. DOI: 10.1016/j.ejrad.2023.111257.
[43]
LI G Z, LI L, LI Y M, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas[J]. Brain, 2022, 145(3): 1151-1161. DOI: 10.1093/brain/awab340.
[44]
JIA X, ZHAI Y X, SONG D X, et al. A multiparametric MRI-based radiomics nomogram for preoperative prediction of survival stratification in glioblastoma patients with standard treatment[J/OL]. Front Oncol, 2022, 12: 58622 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35251957/. DOI: 10.3389/fonc.2022.758622.
[45]
KEON M, DANIEL H K, ELHAM T. Multiparametric radiogenomic model to predict survival in patients with glioblastoma[J/OL]. Cancers (Basel), 2024, 16(3): 589 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38339340/. DOI: 10.3390/cancers16030589.
[46]
WANG Z L, GUAN F Z, DUAN W C. Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features[J]. CNS Neurosci Ther, 2023, 29(11): 3339-3350. DOI: 10.1111/cns.14263.
[47]
XU Y, HE X, LI Y, et al. The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients: A multicenter study[J]. J Magn Reson Imaging, 2021, 54(2): 571-583. DOI: 10.1002/jmri.27536.
[48]
PHILIP R, MARTIN A, CARLO S, et al. From molecular signatures to radiomics: tailoring neurooncological strategies through forecasting of glioma growth[J/OL]. Neurosurg focus, 2024, 56(2): E5 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38301234/. DOI: 10.3171/2023.11.FOCUS23685.
[49]
FU J, SINGHRAO K, ZHONG X R, et al. An automatic deep learning-based workflow for glioblastoma survival prediction using preoperative multimodal MR images: A feasibility study[J/OL]. Adv Radiat Oncol, 2021, 6(5): 100746 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34458648/. DOI: 10.1016/j.adro.2021.100746.
[50]
NIE D, LU J, ZHANG H, et al. Multi‐channel 3D deep feature learning for survival time prediction of brain tumor patients using multi‐modal neuroimages[J/OL]. Sci Rep, 2019, 9(1): 1103 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/30705340/. DOI: 10.1038/s41598-018-37387-9.
[51]
TANG Z, XU Y, JIN L, et al. Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients[J]. IEEE Trans Med Imaging, 2020, 39(6): 2100-2109. DOI: 10.1109/TMI.2020.2964310.
[52]
CHAE J P, KYUNGHWA H, HWIYOUNG K, et al. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas[J]. Eur Radiol, 2020, 30(12): 6464-6474. DOI: 10.1007/s00330-020-07089-w.
[53]
VERDUGO E, PUERTO I, MEDINA M Á. An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment[J]. Cancer Commun, 2022, 42(11): 1083-1111. DOI: 10.1002/cac2.12361.
[54]
MALMSTRÖM A, ŁYSIAK M, ÅKESSON L, et al. ABCB1 single-nucleotide variants and survival in patients with glioblastoma treated with radiotherapy concomitant with temozolomide[J]. Pharmacogenomics J, 2020, 20(2): 213-219. DOI: 10.1038/s41397-019-0107-z.
[55]
KELLY C, MAJEWSKA P, IOANNIDIS S, et al. Estimating progression-free survival in patients with glioblastoma using routinely collected data[J]. J Neurooncol, 2017, 135(3): 621-627. DOI: 10.1007/s11060-017-2619-1.
[56]
DAYTON G, DAVID P, MEGAN C, et al. Clinical and radiographic characteristics of diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma: a single institution review[J]. J Neurooncol, 2022, 157(1): 187-195. DOI: 10.1007/s11060-022-03961-5.
[57]
PARK C J, HAN K, KIM H, et al. MRI features may predict molecular features of glioblastoma in isocitrate dehydrogenase wild-type lower-grade gliomas[J]. AJNR Am J Neuroradiol, 2021, 42(3): 448-456. DOI: 10.3174/ajnr.A6983.
[58]
XIN K, YU M, YU Q L, et al. Machine learning models based on multi-parameter MRI radiomics for prediction of molecular glioblastoma: a new study based on the 2021 World Health Organization classification[J]. Acta Radiol, 2023, 64(11): 2938-2947. DOI: 10.1177/02841851231199744.
[59]
YAE W P, SOOYON K, CHAE J, et al. Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status[J]. Eur Radiol, 2022, 32(12): 8089-8098. DOI: 10.1007/s00330-022-08941-x.
[60]
FUJIMOTO K, ARITA H, SATOMI K, et al. TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma[J]. Acta Neuropathol, 2021, 142(2): 323-338. DOI: 10.1007/s00401-021-02337-9.
[61]
DU P, WU X F, LIU X, et al. Establishment of a prediction model based on preoperative MRI radiomics for diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma[J/OL]. Cancers (Basel), 2023, 15(20): 5094 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37894461/. DOI: 10.3390/cancers15205094.
[62]
PARK Y W, CHOI D, PARK J E, et al. Differentiation of recurrent gli-oblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation[J/OL]. Sci Rep, 2021, 11(1): 2913 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/33536499/. DOI: 10.1038/s41598-021-82467-y.
[63]
JIANG S, GUO P, HEO H Y, et al. Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas[J/OL]. NMR Biomed, 2023, 36(1): e4824 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/36057449/. DOI: 10.1002/nbm.4824.
[64]
ZHANG J, WU Y, WANG Y, et al. Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence[J]. Eur Radiol, 2023, 33(5): 3332-3342. DOI: 10.1007/s00330-022-09365-3.
[65]
REN J F, ZHAI X Y, YIN H J. Multimodality MRI radiomics based on machine learning for identifying true tumor recurrence and treatment-related effects in patients with postoperative glioma[J]. Neurol Ther, 2023, 12(5): 1729-1743. DOI: 10.1007/s40120-023-00524-2.
[66]
MANA M, SHAHRIAR F, GIAN M C, et al. A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients[J]. J Neurooncol, 2022, 159(2): 447-455. DOI: 10.1007/s11060-022-04080-x.
[67]
CHOI S, YU Y, GRIMMER M R, et al. Temozolomide-associated hypermutation in gliomas[J]. Neuro Oncol, 2018, 20(10): 1300-1309. DOI: 10.1093/neuonc/noy016.
[68]
CRAIG H, LOUIS B N, JANA P, et al. NCCN guidelines(R) insights: central nervous system cancers, version 2.2022[J]. J Natl Compr Canc Netw, 2023, 21(1): 12-20. DOI: 10.6004/jnccn.2023.0002.
[69]
GRAMATZKI D, ROTH P, RUSHING E J, et al. Bevacizumab may improve quality of life, but not overall survival in glioblastoma: an epidemiological study[J]. Ann Oncol, 2018, 29(6): 1431-1436. DOI: 10.1093/annonc/mdy106.
[70]
LI Y R, XU W L, FEI Y L, et al. A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients[J/OL]. Discov Oncol, 2023, 23, 14(1): 154 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37612579/. DOI: 10.1007/s12672-023-00751-x.
[71]
WEI J W, YANG G G, HAO X H,et al. A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication[J]. Eur Radiol, 2019, 29(2): 877-888. DOI: 10.1007/s00330-018-5575-z.
[72]
BOBHOLZ S A, HOEFS A, HAMBURGER J, et al. Radio-pathomic maps of glioblastoma identify phenotypes of non-enhancing tumor infiltration associated with bevacizumab treatment response[J]. J Neurooncol, 2024, 167(2): 233-241. DOI: 10.1007/s11060-024-04593-7.
[73]
WANG H, XU T, HUANG Q, et al. Immunotherapy for malignant glioma: Current status and future directions[J]. Trends Pharmacol Sci, 2020, 41(2): 123-138. DOI: 10.1016/j.tips.2019.12.003.
[74]
KE Y Y, ZHI J W, HAO Z, et al. Glioma targeted therapy: insight into future of molecular approaches[J/OL]. Mol Cancer, 2022, 21(1): 39 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35135556/. DOI: 10.1186/s12943-022-01513-z.
[75]
DAPASH M, HOU D, CASTRO B, et al. The interplay between glioblastoma and its microenvironment[J/OL]. Cells, 2021, 10(9): 2257 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34571905/. DOI: 10.3390/cells10092257.
[76]
CHEN D, ZHANG R, HUANG X, et al. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma[J/OL]. Biomark Res, 2024, 12(1): 14 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38291499/. DOI: 10.1186/s40364-024-00560-6.
[77]
LUAN J, ZHANG D, LIU B, et al. Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme[J/OL].J Transl Med, 2024, 22(1): 107 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38279111/. DOI: 10.1186/s12967-023-04823-y.

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