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               ·1284 ·                           南 京    医 科 大 学 学         报                        2023年9月


             [参考文献]                                                  metric analysis to differentiate pseudo ⁃ progression from
                                                                     early tumor progression[J]. J Neurooncol,2013,112(3):
             [1] LOUIS D N,PERRY A,WESSELING P,et al. The 2021
                                                                     413-420
                   WHO classification of tumors of the central nervous sys⁃
                                                                [12] AN G,AHN S,PARK J S,et al. Association between tem⁃
                   tem:a summary[J]. Neuro Oncol,2021,23(8):1231-
                                                                     poral muscle thickness and clinical outcomes in patients
                   1251
                                                                     with newly diagnosed glioblastoma[J]. J Cancer Res Clin
             [2] 国家卫生健康委员会医政医管局. 脑胶质瘤诊疗规范
                                                                     Oncol,2021,147(3):901-909
                  (2018年版)[J]. 中华神经外科杂志,2019,35(3):217-
                                                                [13] FURTNER J,GENBRUGGE E,GORLIA T,et al. Tempo⁃
                   239
                                                                     ral muscle thickness is an independent prognostic marker
             [3] WU X F,LIANG X,WANG X C,et al. Differentiating high⁃
                                                                     in patients with progressive glioblastoma:translational ima⁃
                   grade glioma recurrence from pseudoprogression:compar⁃
                                                                     ging analysis of the EORTC 26101 trial[J]. Neuro Oncol,
                   ing diffusion kurtosis imaging and diffusion tensor imaging
                                                                     2019,21(12):1587-1594
                  [J]. Eur J Radiol,2021,135:109445
                                                                [14] KIM J Y,PARK J E,JO Y,et al. Incorporating diffusion⁃
             [4] BRANDSMA D,VANDEN⁃BENT M J. Pseudoprogression
                                                                     and perfusion⁃weighted MRI into a radiomics model im⁃
                   and pseudoresponse in the treatment of gliomas[J]. Curr
                                                                     proves diagnostic performance for pseudoprogression in
                   Opin Neurol,2009,22(6):633-638
                                                                     glioblastoma patients[J]. Neuro Oncol,2019,21(3):
             [5] WEN P Y,MACDONALD D R,REARDON D A,et al.
                                                                     404-414
                   Updated response assessment criteria for high⁃grade glio⁃
                                                                [15] LOHMANN P,GALLDIKS N,KOCHER M,et al. Ra⁃
                   mas:response assessment in neuro ⁃ oncology working
                                                                     diomics in neuro⁃oncology:basics,workflow,and applica⁃
                   group[J]. J Clin Oncol,2010,28(11):1963-1972
                                                                     tions[J]. Methods,2021,188:112-121
             [6] VAN DIJKEN B,VAN LAAR P J,HOLTMAN G A,et al.
                                                                [16] 陈思璇,许 悦,叶梅萍,等. MRI 不同影像组学模型预
                   Diagnostic accuracy of magnetic resonance imaging tech⁃
                                                                     测胶质瘤MGMT启动子甲基化状态的研究[J]. 磁共振
                   niques for treatment response evaluation in patients with
                                                                     成像,2022,13(3):1-5
                   high⁃grade glioma,a systematic review and meta⁃analysis
                                                                [17] SU C Q,CHEN X T,DUAN S F,et al. A radiomics⁃based
                  [J]. Eur Radiol,2017,27(10):4129-4144
             [7] 苏春秋,韩秋月,周茂冬,等. 动态对比增强MRI纹理分                         model to differentiate glioblastoma from solitary brain me⁃
                   析法与磁敏感加权成像联合应用在脑胶质瘤分级中的                           tastases[J]. Clin Radiol,2021,76(8):629
                                                                [18] 唐   薇,段俊艳,余子意,等. 增强 MRI 影像组学预测
                   价值[J]. 临床放射学杂志,2018,37(8):1264-1268
             [8] 黄晓星,汪泽燕,肖学红,等. 术前MRI强化特征预测胶                         脑胶质瘤 IDH⁃1 基因突变的价值分析[J]. 磁共振成
                   质母细胞瘤患者术后复发风险的价值[J]. 临床放射学                        像,2022,13(5):111-114
                                                                [19] LI Z,MA X,SHEN F,et al. Evaluating treatment response
                   杂志,2022,41(2):217-223
             [9] 唐文天,张梓枫,尹建新,等. 常规MRI特征在弥漫性星                         to neoadjuvant chemoradiotherapy in rectal cancer using
                   形细胞瘤IDH基因突变预测中的临床价值[J]. 南京医                       various MRI⁃based radiomics models[J]. BMC Med Imag⁃
                   科大学学报(自然科学版),2022,42(3):376-381                   ing,2021,21(1):30
                                                                [20] 孙颖志,颜林枫,韩       宇,等. 利用机器学习鉴别胶质母
             [10] SONG Y,ZHANG J,ZHANG Y D,et al. Feature explorer
                                                                     细胞瘤标准化治疗后真假性进展的研究[J]. 神经解剖
                  (FAE):a tool for developing and comparing radiomics
                   models[J]. PLoS One,2020,15(8):e0237587           学杂志,2019,35(2):163-170
             [11] AGARWAL A,KUMAR S,NARANG J,et al. Morphologic                           [收稿日期] 2023-04-30
                   MRI features,diffusion tensor imaging and radiation dosi⁃                   (本文编辑:陈汐敏)
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