1、 LI X,XIONG H,LI X,et al.Interpretable deep learning:interpretation,interpretability,trustworthiness,and beyond[J].Knowl Inf Syst,2022,64(12):3197-3234. LI X,XIONG H,LI X,et al.Interpretable deep learning:interpretation,interpretability,trustworthiness,and beyond[J].Knowl Inf Syst,2022,64(12):3197-3234.
2、 ALI MB,GU IYH,JAKOLA AS.Multi-stream convolutional autoencoder and 2D generative adversarial network for glioma classification[C]//Computer Analysis of Images and Patterns:18th International Conference,CAIP 2019,Salerno,Italy,September 3-5,2019,Proceedings,Part I.ACM,2019:234-245. ALI MB,GU IYH,JAKOLA AS.Multi-stream convolutional autoencoder and 2D generative adversarial network for glioma classification[C]//Computer Analysis of Images and Patterns:18th International Conference,CAIP 2019,Salerno,Italy,September 3-5,2019,Proceedings,Part I.ACM,2019:234-245.
3、 YANG Y,YAN L F,ZHANG X,et al.Glioma grading on conventional MR images:A deep learning study with transfer learning[J].Front Neurosci,2018(12):804. YANG Y,YAN L F,ZHANG X,et al.Glioma grading on conventional MR images:A deep learning study with transfer learning[J].Front Neurosci,2018(12):804.
4、 XIAO T,HUA W,LI C,et al.Glioma grading prediction by exploring radiomics and deep learning features[C]//Proceedings of the Third International Symposium on Image Computing and Digital Medicine.August 24 - 26,2019,Xi’an,China.ACM,2019:208-213. XIAO T,HUA W,LI C,et al.Glioma grading prediction by exploring radiomics and deep learning features[C]//Proceedings of the Third International Symposium on Image Computing and Digital Medicine.August 24 - 26,2019,Xi’an,China.ACM,2019:208-213.
5、 NING Z,LUO J,XIAO Q,et al.Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features[J].Ann Transl Med,2021,9(4):298. NING Z,LUO J,XIAO Q,et al.Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features[J].Ann Transl Med,2021,9(4):298.
6、 YU X,WU Y,BAI Y,et al.A lightweight 3D UNet model for glioma grading[J].Phys Med Biol,2022,67(15). YU X,WU Y,BAI Y,et al.A lightweight 3D UNet model for glioma grading[J].Phys Med Biol,2022,67(15).
7、 CHEN Q,WANG L,WANG L,et al.Glioma grade prediction using wavelet scattering-based radiomics[J].IEEE Access,2020(8):106564-106575. CHEN Q,WANG L,WANG L,et al.Glioma grade prediction using wavelet scattering-based radiomics[J].IEEE Access,2020(8):106564-106575.
8、 CORTES C,VAPNIK V.Support-vector networks[J].Mach Learn,1995,20(3):273-297. CORTES C,VAPNIK V.Support-vector networks[J].Mach Learn,1995,20(3):273-297.
9、 HOSMER DW JR,LEMESHOW S,STURDIVANT RX.Applied Logistic Regression[M].New Jersey:Wiley,2013. HOSMER DW JR,LEMESHOW S,STURDIVANT RX.Applied Logistic Regression[M].New Jersey:Wiley,2013.
10、 UBALDI L,SAPONARO S,GIULIANO A,et al.Deriving quantitative information from multiparametric MRI via Radiomics:evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning[J].Phys Med,2023(107):102538. UBALDI L,SAPONARO S,GIULIANO A,et al.Deriving quantitative information from multiparametric MRI via Radiomics:evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning[J].Phys Med,2023(107):102538.
11、 SUDRE C H,PANOVSKA-GRIFFITHS J,SANVERDI E,et al.Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status[J]. BMC Med Inform Decis Mak,2020,20(1):149. SUDRE C H,PANOVSKA-GRIFFITHS J,SANVERDI E,et al.Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status[J]. BMC Med Inform Decis Mak,2020,20(1):149.
12、 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. 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.
13、 SENGUPTA A,RAMANIHARAN A K,GUPTA R K,et al.Glioma grading using a machine‐learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components[J].J Magn Reson Imaging,2019,50(4):1295-1306. SENGUPTA A,RAMANIHARAN A K,GUPTA R K,et al.Glioma grading using a machine‐learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components[J].J Magn Reson Imaging,2019,50(4):1295-1306.
14、 ALPAYDIN E.Introduction to machine learning[M].Massachusetts:The MIT Press,2020. ALPAYDIN E.Introduction to machine learning[M].Massachusetts:The MIT Press,2020.
15、 ZHOU Z H.Machine Learning[M].Singapore:Springer Singapore,2021. ZHOU Z H.Machine Learning[M].Singapore:Springer Singapore,2021.
16、 BREIMAN L.Random forests[J].Mach Learn,2001(45):5-32. BREIMAN L.Random forests[J].Mach Learn,2001(45):5-32.
17、 GUO J,REN J,SHEN J,et al.Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?[J].Diagn Interv Radiol,2021,27(3):440-449. GUO J,REN J,SHEN J,et al.Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?[J].Diagn Interv Radiol,2021,27(3):440-449.
18、 CHAMBERS J M,HASTIE T J.Statistical models[M]//Chambers J M,Hastie T J,Eds.Statistical Models in S:Routledge,2017:13-44. CHAMBERS J M,HASTIE T J.Statistical models[M]//Chambers J M,Hastie T J,Eds.Statistical Models in S:Routledge,2017:13-44.
19、 SIDDIQUE N,PAHEDING S,ELKIN C P,et al.U-net and its variants for medical image segmentation:a review of theory and applications[J].IEEE Access,2021(9):82031-82057. SIDDIQUE N,PAHEDING S,ELKIN C P,et al.U-net and its variants for medical image segmentation:a review of theory and applications[J].IEEE Access,2021(9):82031-82057.
20、 PENG J,WANG Y.Medical image segmentation with limited supervision:A review of deep network models[J].IEEE Access,2021(9):36827-36851. PENG J,WANG Y.Medical image segmentation with limited supervision:A review of deep network models[J].IEEE Access,2021(9):36827-36851.
21、 GUTTA S,ACHARYA J,SHIROISHI M S,et al.Improved glioma grading using deep convolutional neural networks[J].AJNR Am J Neuroradiol,2021,42(2):233-239. GUTTA S,ACHARYA J,SHIROISHI M S,et al.Improved glioma grading using deep convolutional neural networks[J].AJNR Am J Neuroradiol,2021,42(2):233-239.
22、 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. 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.
23、 COMELLI A,BIGNARDI S,STEFANO A,et al.Development of a new fully three-dimensional methodology for tumours delineation in functional images[J].Comput Biol Med,2020(120):103701. COMELLI A,BIGNARDI S,STEFANO A,et al.Development of a new fully three-dimensional methodology for tumours delineation in functional images[J].Comput Biol Med,2020(120):103701.
24、 JIANG L,ZHOU L,AI Z,et al.Machine learning based on diffusion kurtosis imaging histogram parameters for glioma grading[J].J Clin Med,2022,11(9):2310. JIANG L,ZHOU L,AI Z,et al.Machine learning based on diffusion kurtosis imaging histogram parameters for glioma grading[J].J Clin Med,2022,11(9):2310.
25、 AHAMMED MUNEER K V,RAJENDRAN VR,PAUL JOSEPH K.Glioma tumor grade identification using artificial intelligent techniques[J].J Med Syst,2019,43(5):113. AHAMMED MUNEER K V,RAJENDRAN VR,PAUL JOSEPH K.Glioma tumor grade identification using artificial intelligent techniques[J].J Med Syst,2019,43(5):113.
26、 LIN K,CIDAN W,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. LIN K,CIDAN W,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.
27、 VAMVAKAS A,WILLIAMS S C,THEODOROU K,et al.Imaging biomarker analysis of advanced multiparametric MRI for glioma grading[J].Phys Med,2019(60):188-198. VAMVAKAS A,WILLIAMS S C,THEODOROU K,et al.Imaging biomarker analysis of advanced multiparametric MRI for glioma grading[J].Phys Med,2019(60):188-198.
28、 LIU J,LI C,CHEN Y,et al.Diagnostic performance of multiparametric MRI in the evaluation of treatment response in glioma patients at 3T[J].J Magn Reson Imaging,2020,51(4):1154-1161. LIU J,LI C,CHEN Y,et al.Diagnostic performance of multiparametric MRI in the evaluation of treatment response in glioma patients at 3T[J].J Magn Reson Imaging,2020,51(4):1154-1161.
29、 TAKAHASHI S,TAKAHASHI W,TANAKA S,et al.Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging[J].Int J Radiat Oncol Biol Phys,2019,105(4):784-791. TAKAHASHI S,TAKAHASHI W,TANAKA S,et al.Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging[J].Int J Radiat Oncol Biol Phys,2019,105(4):784-791.
30、 WANG Q,LI Q,MI R,et al.Radiomics nomogram building from multiparametric MRI to predict grade in patients with glioma:A cohort study[J].J Magn Reson Imaging,2019,49(3):825-833. WANG Q,LI Q,MI R,et al.Radiomics nomogram building from multiparametric MRI to predict grade in patients with glioma:A cohort study[J].J Magn Reson Imaging,2019,49(3):825-833.
31、 SU C,JIANG J,ZHANG S,et al.Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour[J].Eur Radiol,2019,29(4):1986-1996. SU C,JIANG J,ZHANG S,et al.Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour[J].Eur Radiol,2019,29(4):1986-1996.
32、 CHENG J,LIU J,YUE H,et al.Prediction of glioma grade using intratumoral and peritumoral radiomic features from multiparametric MRI images[J].IEEE/ACM Trans Comput Biol Bioinform,2022,19(2):1084-1095. CHENG J,LIU J,YUE H,et al.Prediction of glioma grade using intratumoral and peritumoral radiomic features from multiparametric MRI images[J].IEEE/ACM Trans Comput Biol Bioinform,2022,19(2):1084-1095.
33、 CHO H H,LEE S H,KIM J,et al.Classification of the glioma grading using radiomics analysis[J].PeerJ,2018(6):e5982. CHO H H,LEE S H,KIM J,et al.Classification of the glioma grading using radiomics analysis[J].PeerJ,2018(6):e5982.
34、 ZHAO S S,FENG X L,HU Y C,et al.Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images[J].BMC Neurol,2020,20(1):48. ZHAO S S,FENG X L,HU Y C,et al.Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images[J].BMC Neurol,2020,20(1):48.
35、 NAKAMOTO T,TAKAHASHI W,HAGA A,et al.Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis[J].Sci Rep,2019,9(1):19411. NAKAMOTO T,TAKAHASHI W,HAGA A,et al.Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis[J].Sci Rep,2019,9(1):19411.
36、 BRYNOLFSSON P,NILSSON D,HENRIKSSON R,et al.ADC texture:An imaging biomarker for high‐grade glioma?[J].Med Phys,2014,41(10):101903. BRYNOLFSSON P,NILSSON D,HENRIKSSON R,et al.ADC texture:An imaging biomarker for high‐grade glioma?[J].Med Phys,2014,41(10):101903.
37、 秦倩,高翾,张岚,等.基于3D APTw 影像组学模型预测脑胶质瘤 IDH 突变状态及 WHO 分级的研究[J].中国临床新医学,2023,16(4):336-341. 秦倩,高翾,张岚,等.基于3D APTw 影像组学模型预测脑胶质瘤 IDH 突变状态及 WHO 分级的研究[J].中国临床新医学,2023,16(4):336-341.
38、 SAKATA A,OKADA T,YAMAMOTO A,et al.Grading glial tumors with amide proton transfer MR imaging:Different analytical approaches[J].J Neurooncol,2015,122(2):339-348. SAKATA A,OKADA T,YAMAMOTO A,et al.Grading glial tumors with amide proton transfer MR imaging:Different analytical approaches[J].J Neurooncol,2015,122(2):339-348.
39、 JEONG J,WANG L,JI B,et al.Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images:Introduction[J].Quant Imaging Med Surg,2019,9(7):1201-1213. JEONG J,WANG L,JI B,et al.Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images:Introduction[J].Quant Imaging Med Surg,2019,9(7):1201-1213.
40、 ANZALONE N,CASTELLANO A,CADIOLI M,et al.Brain gliomas:Multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images[J].Radiology,2018,287(3):933-943. ANZALONE N,CASTELLANO A,CADIOLI M,et al.Brain gliomas:Multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images[J].Radiology,2018,287(3):933-943.
41、 ZHAO P F,XIE S H,QIAO P F,et al.Role of texture analysis and dynamic contrast-enhanced magnetic resonance imaging quantitative parameters based on different regions of interest in glioma grading[J].Asian J Surg,2021,44(8):1089-1090. ZHAO P F,XIE S H,QIAO P F,et al.Role of texture analysis and dynamic contrast-enhanced magnetic resonance imaging quantitative parameters based on different regions of interest in glioma grading[J].Asian J Surg,2021,44(8):1089-1090.
42、 XIE T,CHEN X,FANG J,et al.Textural features of dynamic contrast‐enhanced MRI derived model‐free and model‐based parameter maps in glioma grading[J].J Magn Reson Imag,2018,47(4):1099-1111. XIE T,CHEN X,FANG J,et al.Textural features of dynamic contrast‐enhanced MRI derived model‐free and model‐based parameter maps in glioma grading[J].J Magn Reson Imag,2018,47(4):1099-1111.
43、 HYLTON N.Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker[J].J Clin Oncol,2006,24(20):3293-3298. HYLTON N.Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker[J].J Clin Oncol,2006,24(20):3293-3298.
44、 韩彤,郭军,刘力,等.动态对比增强磁共振灌注成像在脑肿瘤诊断中的应用价值[J].中国现代神经疾病杂志,2006,6(3):211-219. 韩彤,郭军,刘力,等.动态对比增强磁共振灌注成像在脑肿瘤诊断中的应用价值[J].中国现代神经疾病杂志,2006,6(3):211-219.
45、 NUCCI C,GARACI F,ALTOBELLI S,et al.Diffusional kurtosis imaging of white matter degeneration in glaucoma[J].J Clin Med,2020,9(10):3122. NUCCI C,GARACI F,ALTOBELLI S,et al.Diffusional kurtosis imaging of white matter degeneration in glaucoma[J].J Clin Med,2020,9(10):3122.
46、 潘虹,陈燕萍,易云平.DTI在颅脑胶质瘤术前分级中的应用[J].中国中西医结合影像学杂志,2020,18(1):26-29. 潘虹,陈燕萍,易云平.DTI在颅脑胶质瘤术前分级中的应用[J].中国中西医结合影像学杂志,2020,18(1):26-29.
47、 孙功能,朱虎,邱伟,等.磁共振弥散张量成像定量参数在胶质瘤分级诊断中的应用研究[J].医学影像学杂志,2019,29(4):528-531. 孙功能,朱虎,邱伟,等.磁共振弥散张量成像定量参数在胶质瘤分级诊断中的应用研究[J].医学影像学杂志,2019,29(4):528-531.
48、 WANG S,MENG M,ZHANG X,et al.Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest[J].Oncol Lett,2018,15(5):7297-7304. WANG S,MENG M,ZHANG X,et al.Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest[J].Oncol Lett,2018,15(5):7297-7304.
49、 SOLIMAN R K,ESSA A A,ELHAKEEM A A,et al.Texture analysis of apparent diffusion coefficient(ADC)map for glioma grading:Analysis of whole tumoral and peri-tumoral tissue[J].Diagn Interv Imag,2021,102(5):287-295. SOLIMAN R K,ESSA A A,ELHAKEEM A A,et al.Texture analysis of apparent diffusion coefficient(ADC)map for glioma grading:Analysis of whole tumoral and peri-tumoral tissue[J].Diagn Interv Imag,2021,102(5):287-295.
50、 GIHR G A,HORVATH-RIZEA D,HEKELER E,et al.Histogram analysis of diffusion weighted imaging in low-grade gliomas:In vivo characterization of tumor architecture and corresponding neuropathology[J].Front Oncol,2020(10):206. GIHR G A,HORVATH-RIZEA D,HEKELER E,et al.Histogram analysis of diffusion weighted imaging in low-grade gliomas:In vivo characterization of tumor architecture and corresponding neuropathology[J].Front Oncol,2020(10):206.
51、 ZHANG L,MIN Z,TANG M,et al.The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas:Evidence from a meta-analysis[J].J Neurol Sci,2017(373):9-15. ZHANG L,MIN Z,TANG M,et al.The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas:Evidence from a meta-analysis[J].J Neurol Sci,2017(373):9-15.
52、 KUSUNOKI M,KIKUCHI K,TOGAO O,et al.Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging:A comparative study of mono-,Bi-,and stretched-exponential diffusion models[J].Neuroradiology,2020,62(7):815-823. KUSUNOKI M,KIKUCHI K,TOGAO O,et al.Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging:A comparative study of mono-,Bi-,and stretched-exponential diffusion models[J].Neuroradiology,2020,62(7):815-823.
53、 MANNELLI L,NOUGARET S,VARGAS H A,et al.Advances in diffusion-weighted imaging[J].Radiol Clin North Am,2015,53(3):569-581. MANNELLI L,NOUGARET S,VARGAS H A,et al.Advances in diffusion-weighted imaging[J].Radiol Clin North Am,2015,53(3):569-581.
54、 张微,牛蕾,马敏阁,等.DCE-MRI在高、低级别脑胶质瘤及脑膜瘤中的鉴别诊断[J].磁共振成像,2015,6(8):566-570. 张微,牛蕾,马敏阁,等.DCE-MRI在高、低级别脑胶质瘤及脑膜瘤中的鉴别诊断[J].磁共振成像,2015,6(8):566-570.
55、 SU C,CHEN X,LIU C,et al.T2-FLAIR,DWI and DKI radiomics satisfactorily predicts histological grade and Ki-67 proliferation index in gliomas[J].Am J Transl Res,2021,13(8):9182-9194. SU C,CHEN X,LIU C,et al.T2-FLAIR,DWI and DKI radiomics satisfactorily predicts histological grade and Ki-67 proliferation index in gliomas[J].Am J Transl Res,2021,13(8):9182-9194.
56、 XU J,LAI M,LI S,et al.Noninvasive prediction of histological grading in pediatric low-grade gliomas using preoperative T2-FLAIR radiomics features[J].World Neurosurg,2023(177):e34-e43. XU J,LAI M,LI S,et al.Noninvasive prediction of histological grading in pediatric low-grade gliomas using preoperative T2-FLAIR radiomics features[J].World Neurosurg,2023(177):e34-e43.
57、 TRIPATHI P C,BAG S.A computer-aided grading of glioma tumor using deep residual networks fusion[J].Comput Meth Programs Biomed,2022(215):106597. TRIPATHI P C,BAG S.A computer-aided grading of glioma tumor using deep residual networks fusion[J].Comput Meth Programs Biomed,2022(215):106597.
58、 AL-ZURFI A N,MEZIANE F,ASPIN R.A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour[C]//2019 3rd International Conference on Bio-engineering for Smart Technologies(BioSMART).Paris,France.IEEE,2019:1-5. AL-ZURFI A N,MEZIANE F,ASPIN R.A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour[C]//2019 3rd International Conference on Bio-engineering for Smart Technologies(BioSMART).Paris,France.IEEE,2019:1-5.
59、 TOVI M.MR imaging in cerebral gliomas analysis of tumour tissue components[J].Acta Radiol Suppl,1993(384):1-24. TOVI M.MR imaging in cerebral gliomas analysis of tumour tissue components[J].Acta Radiol Suppl,1993(384):1-24.
60、 赵博涵,王训恒,厉力华.基于T1加权磁共振影像组学的脑胶质瘤分级[J].杭州电子科技大学学报(自然科学版),2023,43(2):61-66. 赵博涵,王训恒,厉力华.基于T1加权磁共振影像组学的脑胶质瘤分级[J].杭州电子科技大学学报(自然科学版),2023,43(2):61-66.
61、 DITMER A,ZHANG B,SHUJAAT T,et al.Diagnostic accuracy of MRI texture analysis for grading gliomas[J].J Neurooncol,2018,140(3):583-589. DITMER A,ZHANG B,SHUJAAT T,et al.Diagnostic accuracy of MRI texture analysis for grading gliomas[J].J Neurooncol,2018,140(3):583-589.
62、 ZHANG H,ZHANG B,PAN W,et al.Preoperative contrast-enhanced MRI in differentiating glioblastoma from low-grade gliomas in the cancer imaging archive database:A proof-of-concept study[J].Front Oncol,2022(11):761359. ZHANG H,ZHANG B,PAN W,et al.Preoperative contrast-enhanced MRI in differentiating glioblastoma from low-grade gliomas in the cancer imaging archive database:A proof-of-concept study[J].Front Oncol,2022(11):761359.
63、 ZHOU H,XU R,MEI H,et al.Application of enhanced T1WI of MRI radiomics in glioma grading[J].Int J Clin Pract,2022(2022):3252574. ZHOU H,XU R,MEI H,et al.Application of enhanced T1WI of MRI radiomics in glioma grading[J].Int J Clin Pract,2022(2022):3252574.
64、 GAO M,HUANG S,PAN X,et al.Machine learning-based radiomics predicting tumor grades and expression of multiple pathologic biomarkers in gliomas[J].Front Oncol,2020(10):1676. GAO M,HUANG S,PAN X,et al.Machine learning-based radiomics predicting tumor grades and expression of multiple pathologic biomarkers in gliomas[J].Front Oncol,2020(10):1676.
65、 ZHOU H,VALLIèRES M,BAI H X,et al.MRI features predict survival and molecular markers in diffuse lower-grade gliomas[J].Neuro-oncology,2017,19(6):862-870. ZHOU H,VALLIèRES M,BAI H X,et al.MRI features predict survival and molecular markers in diffuse lower-grade gliomas[J].Neuro-oncology,2017,19(6):862-870.
66、 KICKINGEREDER P,BURTH S,WICK A,et al.Radiomic profiling of glioblastoma:Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[J].Radiology,2016,280(3):880-889. KICKINGEREDER P,BURTH S,WICK A,et al.Radiomic profiling of glioblastoma:Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models[J].Radiology,2016,280(3):880-889.
67、 CHA S.Neuroimaging in neuro-oncology[J].Neurotherapeutics,2009,6(3):465-477. CHA S.Neuroimaging in neuro-oncology[J].Neurotherapeutics,2009,6(3):465-477.
68、 WEN P Y,KESARI S.Malignant gliomas in adults[J].N Engl J Med,2008,359(5):492-507. WEN P Y,KESARI S.Malignant gliomas in adults[J].N Engl J Med,2008,359(5):492-507.
69、 MAYERHOEFER M E,MATERKA A,LANGS G,et al.Introduction to radiomics[J]. J Nucl Med,2020,61(4):488-495. MAYERHOEFER M E,MATERKA A,LANGS G,et al.Introduction to radiomics[J]. J Nucl Med,2020,61(4):488-495.
70、 PARMAR C,GROSSMANN P,RIETVELD D,et al.Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer[J].Front Oncol,2015(5):272. PARMAR C,GROSSMANN P,RIETVELD D,et al.Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer[J].Front Oncol,2015(5):272.
71、 LIU Z,MENG X,ZHANG H,et al.Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer[J].Nat Commun,2020,11(1):4308. LIU Z,MENG X,ZHANG H,et al.Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer[J].Nat Commun,2020,11(1):4308.
72、 JIANG Y,CHEN C,XIE J,et al.Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer[J].EBioMedicine,2018(36):171-182. JIANG Y,CHEN C,XIE J,et al.Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer[J].EBioMedicine,2018(36):171-182.
73、 于永成,姜雨萌,方玲玲.基于胶质瘤图像的原始数据集构建及应用[J].计算机系统应用,2023,32(1):368-375. 于永成,姜雨萌,方玲玲.基于胶质瘤图像的原始数据集构建及应用[J].计算机系统应用,2023,32(1):368-375.
74、 KUMAR V,GU Y,BASU S,et al.Radiomics:The process and the challenges[J].Magn Reson Imag,2012,30(9):1234-1248. KUMAR V,GU Y,BASU S,et al.Radiomics:The process and the challenges[J].Magn Reson Imag,2012,30(9):1234-1248.
75、 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. 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.
76、 LAMBIN P,LEIJENAAR R T,DEIST T M,et al.Radiomics:The bridge between medical imaging and personalized medicine[J].Nat Rev Clin Oncol,2017,14(12):749-762. LAMBIN P,LEIJENAAR R T,DEIST T M,et al.Radiomics:The bridge between medical imaging and personalized medicine[J].Nat Rev Clin Oncol,2017,14(12):749-762.
77、 GILLIES R J,KINAHAN P E,HRICAK H.Radiomics:Images are more than pictures,they are data[J].J Radiat Res,2016,278(2):563-577. GILLIES R J,KINAHAN P E,HRICAK H.Radiomics:Images are more than pictures,they are data[J].J Radiat Res,2016,278(2):563-577.
78、 WU J,THA K K,XING L,et al.Radiomics and radiogenomics for precision radiotherapy[J].Journal of radiation research,2018,59(suppl_1):i25-i31. WU J,THA K K,XING L,et al.Radiomics and radiogenomics for precision radiotherapy[J].Journal of radiation research,2018,59(suppl_1):i25-i31.
79、 AERTS H J,VELAZQUEZ E R,LEIJENAAR R T,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J].Nat Commun,2014(5):4006. AERTS H J,VELAZQUEZ E R,LEIJENAAR R T,et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J].Nat Commun,2014(5):4006.
80、 钱春红,沈海林,房志伟,等.不同病理级别脑胶质瘤的MRI表现[J].现代医用影像学,2019,28(8):1728-1730. 钱春红,沈海林,房志伟,等.不同病理级别脑胶质瘤的MRI表现[J].现代医用影像学,2019,28(8):1728-1730.
81、 VLAARDINGERBROEK M T,den BOER J A.Magnetic resonance imaging:Theory and practice[M].Berlin,Heidelberg:Springer Berlin Heidelberg,1996. VLAARDINGERBROEK M T,den BOER J A.Magnetic resonance imaging:Theory and practice[M].Berlin,Heidelberg:Springer Berlin Heidelberg,1996.
82、 JACKSON R J,FULLER G N,ABI-SAID D,et al.Limitations of stereotactic biopsy in the initial management of gliomas[J].Neuro-oncology,2001,3(3):193-200. JACKSON R J,FULLER G N,ABI-SAID D,et al.Limitations of stereotactic biopsy in the initial management of gliomas[J].Neuro-oncology,2001,3(3):193-200.
83、 LOHMANN P,GALLDIKS N,KOCHER M,et al.Radiomics in neuro-oncology:basics,workflow,and applications[J].Methods,2021(188):112-121. LOHMANN P,GALLDIKS N,KOCHER M,et al.Radiomics in neuro-oncology:basics,workflow,and applications[J].Methods,2021(188):112-121.
84、 ANEJA S,CHANG E,OMURO A.Applications of artificial intelligence in neuro-oncology[J].Curr Opin Neurol,2019,32(6):850-856. ANEJA S,CHANG E,OMURO A.Applications of artificial intelligence in neuro-oncology[J].Curr Opin Neurol,2019,32(6):850-856.
85、 HAJ-HOSSEINI N,RICHTER J C,MILOS P,et al.5-ALA fluorescence and laser Doppler flowmetry for guidance in a stereotactic brain tumor biopsy[J].Biomed Opt Express,2018,9(5):2284-2296. HAJ-HOSSEINI N,RICHTER J C,MILOS P,et al.5-ALA fluorescence and laser Doppler flowmetry for guidance in a stereotactic brain tumor biopsy[J].Biomed Opt Express,2018,9(5):2284-2296.
86、 A GHOTME K,E BARRETO G,ECHEVERRIA V,et al.Gliomas:New perspectives in diagnosis,treatment and prognosis[J].Curr Top Med Chem,2017,17(12):1438-1447. A GHOTME K,E BARRETO G,ECHEVERRIA V,et al.Gliomas:New perspectives in diagnosis,treatment and prognosis[J].Curr Top Med Chem,2017,17(12):1438-1447.
87、 LOUIS D N,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. LOUIS D N,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.