Page 178 - 磁共振成像2024年7期电子刊
P. 178
磁共振成像 2024年7月第15卷第7期 Chin J Magn Reson Imaging, Jul, 2024, Vol. 15, No. 7 综 述||Reviews
with proton magnetic resonance spectroscopy and diffusion tensor imaging[J]. Comput Biol Med, 2023, 153: 106492 [2024-03-04]. https://pubmed.
Med Phys, 2022, 49(7): 4419-4429. DOI: 10.1002/mp.15648. ncbi.nlm.nih.gov/36621191/. DOI: 10.1016/j.compbiomed.2022.106492.
[10] WANG P, XIE S H, WU Q, et al. Model incorporating multiple diffusion [29] LOUIS D N, PERRY A, WESSELING P, et al. Pre-operative MRI
MRI features: development and validation of a radiomics-based model to radiomics model non-invasively predicts key genomic markers and
predict adult-type diffuse gliomas grade[J]. Eur Radiol, 2023, 33(12): survival in glioblastoma patients[J]. J Neurooncol, 2022, 160(1):
8809-8820. DOI: 10.1007/s00330-023-09861-0. 253-263. DOI: 10.1007/s11060-022-04150-0.
[11] ZHU F Y, SUNY F, YIN X P. Use of Radiomics models in preoperative [30] CHEN S X, XU Y, YE M P, et al. Predicting MGMT promoter
grading of cerebral gliomas and comparison with three-dimensional methylation in diffuse gliomas using deep learning with radiomics[J/OL]. J
arterial spin labelling[J]. Clin Oncol (R Coll Radiol), 2023, 35(11): Clin Med, 2022, 11(12): 3445 [2024-03-04]. https://pubmed.ncbi.nlm.
726-735. DOI: 10.1016/j.clon.2023.08.001. nih.gov/35743511/. DOI: 10.3390/jcm11123445.
[12] SEBASTIAN R, FATIH I, MAARTEN M, et al. Combined molecular [31] HUANG L E. Impact of CDKN2A/B homozygous deletion on the
subtyping, grading, and segmentation of glioma using multi-task deep prognosis and biology of IDH-mutant glioma[J/OL]. Biomedicine,
learning[J]. Neuro Oncol, 2023, 25(2): 279-289. DOI:10.1093/neuonc/ 2022, 10(2): 246 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35203456/.
noac166. DOI: 10.3390/biomedicines10020246.
[13] SREEJITH V I, BUDHIRAJU V V, YOGEESWARI P, et al. Deep [32] LOUIS D, PERRY A, WESSELING P, et al. The 2021 WHO
learning classifies low- and high-grade glioma patients with high classification of tumors of the central nervous system: a summary[J].
accuracy, sensitivity, and specificity based on their brain white matter Neuro Oncol, 2021, 23(8): 1231-1251. DOI: 10.1093/neuonc/noab106.
networks derived from diffusion tensor imaging[J/OL]. Diagnostics [33] YAE W P, KI S P, JI E P, et al. Qualitative and quantitative magnetic
(Basel), 2022, 12(12): 3216 [2024-03-04]. https://pubmed.ncbi.nlm.nih. resonance imaging phenotypes may predict CDKN2A/B homozygous
gov/36553224/. DOI: 10.3390/diagnostics12123216. deletion status in isocitrate dehydrogenase-mutant astrocytomas: A
[14] ZHANG Z, XIAO J, WU S, et al. Deep convolutional radiomic features on multicenter study[J]. Korean J Radiol, 2023, 24(2): 133-144. DOI:
diffusion tensor images for classification of glioma grades[J]. J Digit 10.3348/kjr.2022.0732.
Imaging, 2020, 33(4): 826-837. DOI: 10.1007/s10278-020-00322-4. [34] LI Q Z, RUI W, JUENI G. A novel MRI-based deep learning
[15] MZOUGHI H, NJEH I, WALI A, et al. Deep multi ‐ scale 3D networks combined with attention mechanism for predicting CDKN2A/
convolutional neural network (CNN) for MRI gliomas brain tumor B homozygous deletion status in IDH-mutant astrocytoma[J]. Eur
classification[J]. J Digit Imaging, 2020, 33(4): 903-915. DOI: 10.1007/ Radiol, 2024, 34(1): 391-399. DOI: 10.1007/s00330-023-09944-y.
s10278-020-00347-9. [35] GAO J N, LIU Z, PAN H Y, et al. Preoperative discrimination of
[16] KILLELA P J, PIROZZI C J, HEALY P. Mutations in IDH1, IDH2, CDKN2A/B homozygous deletion status in isocitrate dehydrogenase-mutant
and in the TERT promoter define clinically distinct subgroups of adult astrocytoma: A deep learning-based radiomics model using MRI[J]. J
malignant gliomas[J]. Oncotarget, 2014, 5(6): 1515-1525. DOI: 10.18632/ Magn Reson Imaging, 2024, 59(5): 1655-1664. DOI: 10.1002/jmri.28945.
oncotarget.1765. [36] GAO K, LI G, QU Y, et al. TERT promoter mutations and long telomere
[17] ŚLEDZIŃSKA P, BEBYN M G, FURTAK J, et al. Prognostic and length predict poor survival and radiotherapy resistance in gliomas[J].
predictive biomarkers in gliomas[J/OL]. Int J Mol Sci, 2021, 22(19): Oncotarget, 2016, 7(8): 8712-8725. DOI: 10.18632/oncotarget.6007.
10373 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34638714/. DOI: [37] LU J, LI X, LI H, et al. A radiomics feature-based nomogram to predict
10.3390/ijms221910373. telomerase reverse transcriptase promoter mutation status and the
[18] ZHANG H, OUYANG Y, ZHANG H, et al. Sub-region based radiomics prognosis of lower-grade gliomas[J/OL]. Clin Radiol, 2022, 77(8):
analysis for prediction of isocitrate dehydrogenase and telomerase reverse e560-e567 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35595562/.
transcriptase promoter mutations in diffuse gliomas[J/OL]. Clin Radiol, DOI: 10.1016/j.crad.2022.04.005.
2024, 8: S0009-9260(24)00081-3 [2024-03-04]. https://pubmed. ncbi. nlm. [38] HUO X L, WANG Y L, MA S H, et al. Multimodal MRI-based radiomic
nih.gov/38402087/. DOI: 10.1016/j.crad.2024.01.030. nomogram for predicting telomerase reverse transcriptase promoter
[19] YUAN Z L, WEN J L, DONG B. The value of multiparametric MRI mutation in IDH-wildtype histological lower-grade gliomas[J/OL].
radiomics in predicting IDH genotype in glioma before surgery[J/OL]. Medicine (Baltimore), 2023, 102(51): e36581 [2024-03-04]. https://pubmed.
Front Oncol, 2023, 13: 1265672 [2024-03-04]. https://pubmed.ncbi. ncbi.nlm.nih.gov/38134061/. DOI: 10.1097/MD.0000000000036581.
nlm.nih.gov/38090497/. DOI: 10.3389/fonc.2023.1265672. [39] LI Z C, KAISER L, HOLZGREVE A, et al. Prediction of
[20] GIANFRANCO D S, LORENZO T, MARIA E L, et al. Accuracy of TERTp-mutation status in IDH-wildtype high-grade gliomas using
radiomics in predicting IDH mutation status in diffuse gliomas: A pre-treatment dynamic [(18)F]FET PET radiomics[J]. Eur J Nucl Med
bivariate meta-analysis[J/OL]. Radiol Artif Intell, 2024, 6(1): e220257 Mol Imaging, 2021, 48(13): 4415-4425. DOI: 10.1007/s00259-021-05526-6.
[2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38231039/. DOI: 10.1148/ [40] YAN J, ZHANG B, ZHANG S, et al. Quantitative MRI-based
ryai.220257. radiomics for noninvasively predicting molecular subtypes and
[21] IOANNIDIS G S, PIGOTT L E, IV M, et al. Investigating the value of survival in glioma patients[J/OL]. NPJ Precis Oncol, 2021, 5(1): 72
radiomics stemming from DSC quantitative biomarkers in IDH [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34312469/. DOI: 10.1038/
mutation prediction in gliomas[J/OL]. Front Neurol, 2023, 14: 1249452 s41698-021-00205-z.
[2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38046592/. DOI: 10.3389/ [41] ZHANG H B, ZHOU B B, ZHANG H W, et al. Peritumoral radiomics
fneur.2023.1249452. for identification of telomerase reverse transcriptase promoter mutation
[22] LU J, XU W J, CHEN X C, et al. Noninvasive prediction of IDH in patients with glioblastoma based on preoperative MRI[J]. Can Assoc
mutation status in gliomas using preoperative multiparametric MRI Radiol J, 2024, 75(1): 143-152. DOI: 10.1177/08465371231183309.
radiomics nomogram: A mutlicenter study[J]. Magn Reson Imaging, [42] BUZ-YALUG B, TURHAN G, CETIN A I, et al. Identification of IDH
2023, 104: 72-79. DOI: 10.1016/j.mri.2023.09.001. and TERTp mutations using dynamic susceptibility contrast MRI with
[23] WANG J, HU Y, ZHOU X J, et al. A radiomics model based on deep learning in 162 gliomas[J/OL].Eur J Radiol, 2024, 170: 111257
DCE-MRI and DWI may improve the prediction of estimating IDH1 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38134710/. DOI: 10.1016/
mutation and angiogenesis in gliomas[J/OL]. Eur J Radiol, 2022, 47: j.ejrad.2023.111257.
110141 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/34995947/. DOI: [43] LI G Z, LI L, LI Y M, et al. An MRI radiomics approach to predict
10.1016/j.ejrad.2021.110141. survival and tumour-infiltrating macrophages in gliomas[J]. Brain,
[24] PASQUINI L, NAPOLITANO A, TAGLIENTE E, et al. Deep learning 2022, 145(3): 1151-1161. DOI: 10.1093/brain/awab340.
can differentiate IDH-mutant from IDH-wild GBM[J/OL]. J Pers Med, [44] JIA X, ZHAI Y X, SONG D X, et al. A multiparametric MRI-based
2021, 11(4): 290 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/33918828/. radiomics nomogram for preoperative prediction of survival stratification in
DOI: 10.3390/jpm11040290. glioblastoma patients with standard treatment[J/OL]. Front Oncol, 2022, 12:
[25] LI Z J, WANG Y Y, YU J H, et al. Deep learning based radiomics 58622 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/35251957/. DOI:
(DLR) and its usage in noninvasive IDH1 prediction for low grade 10.3389/fonc.2022.758622.
glioma[J/OL]. Sci Rep, 2017, 7(1):5467 [2024-03-04]. https://pubmed. [45] KEON M, DANIEL H K, ELHAM T. Multiparametric radiogenomic
ncbi.nlm.nih.gov/28710497/. DOI: 10.1038/s41598-017-05848-2. model to predict survival in patients with glioblastoma[J/OL]. Cancers
[26] BANGALORE YOGANANDA C G, WAGNER B C, TRUONG N C D, (Basel), 2024, 16(3): 589 [2024-03-04]. https://pubmed. ncbi. nlm. nih.
et al. MRI-based deep learning method for classification of IDH gov/38339340/. DOI: 10.3390/cancers16030589.
mutation status[J/OL]. Bioengineering (Basel), 2023, 10(9): 1045 [46] WANG Z L, GUAN F Z, DUAN W C. Diffusion tensor imaging-based
[2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/37760146/. DOI: 10.3390/ machine learning for IDH wild-type glioblastoma stratification to
bioengineering reveal the biological underpinning of radiomic features[J]. CNS Neurosci
10091045. Ther, 2023, 29(11): 3339-3350. DOI: 10.1111/cns.14263.
[27] DONISELLI F M, PASCUZZO R, AGRÒ M, et al. Development of a [47] XU Y, HE X, LI Y, et al. The nomogram of MRI-based radiomics with
radiomic model for MGMT promoter methylation detection in complementary visual features by machine learning improves stratification
glioblastoma using conventional MRI[J/OL]. Int J Mol Sci, 2023, of glioblastoma patients: A multicenter study[J]. J Magn Reson Imaging,
25(1): 138 [2024-03-04]. https://pubmed.ncbi.nlm.nih.gov/38203308/. 2021, 54(2): 571-583. DOI: 10.1002/jmri.27536.
DOI: 10.3390/ijms25010138. [48] PHILIP R, MARTIN A, CARLO S, et al. From molecular signatures
[28] SAXENA S, JENA B, MOHAPATRA B, et al. Fused deep learning to radiomics: tailoring neurooncological strategies through forecasting
6
paradigm for the prediction of O -methylguanine-DNA methyltransferase of glioma growth[J/OL]. Neurosurg focus, 2024, 56(2): E5 [2024-03-04].
genotype in glioblastoma patients:A neuro-oncological investigation[J/OL]. https://pubmed.ncbi.nlm.nih.gov/38301234/. DOI: 10.3171/2023.11.FOCUS
https://www.chinesemri.com ·171 ·