| [1] |
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.
|
| [2] |
Wu CC, Jain R, Neto L, et al. MR imaging phenotype correlates with extent of genome-wide copy number abundance in IDH mutant gliomas[J]. Neuroradiology, 2019, 61(9): 1023-1031.
|
| [3] |
Jiang T, Mao Y, Ma W, et al. CGCG clinical practice guidelines for the management of adult diffuse gliomas[J]. Cancer Lett, 2016, 375(2): 263-273.
|
| [4] |
Lawrence DR, Palacios-González C, Harris J. Artificial intelligence[J]. Camb Q Healthc Ethics, 2016, 25(2): 250-261.
|
| [5] |
Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics[J]. Genome Med, 2019, 11(1): 70.
|
| [6] |
Peek N, Combi C, Marin R, et al. Thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes[J]. Artif Intell Med, 2015, 65(1): 61-73.
|
| [7] |
Adlassnig KP. Artificial-intelligence-augmented systems[J]. Artif Intell Med, 2002, 24(1): 1-4.
|
| [8] |
Ngu JC, Sim S, Yusof S, et al. Insight into the da Vinci®Xi-technical notes for single-docking left-sided colorectal procedures[J]. Int J Med Robot, 2017, 13(4): 10.1002/rcs.1798. |
| [9] |
Sirintrapun SJ, Lopez AM. Telemedicine in cancer care[J]. Am Soc Clin Oncol Educ Book, 2018, 38: 540-545.
|
| [10] |
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118.
|
| [11] |
Ben-Cohen A, Mechrez R, Yedidia N, et al. Improving CNN training using disentanglement for liver lesion classification in CT[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2019, 2019: 886-889.
|
| [12] |
Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters[J]. Biostatistics, 2019, 20(2): 273-286.
|
| [13] |
Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety[J]. Trends Pharmacol Sci, 2019, 40(9): 624-635.
|
| [14] |
Carelli L, Solca F, Faini A, et al. Brain-computer interface for clinical purposes: cognitive assessment and rehabilitation[J]. Biomed Res Int, 2017, 2017: 1695290.
|
| [15] |
Shih JJ, Krusienski DJ, Wolpaw JR. Brain-computer interfaces in medicine[J]. Mayo Clin Proc, 2012, 87(3): 268-279.
|
| [16] |
Baek HJ, Chang MH, Heo J, et al. Enhancing the usability of brain-computer interface systems[J]. Comput Intell Neurosci, 2019, 2019: 5427154.
|
| [17] |
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510.
|
| [18] |
Longo DL. Tumor heterogeneity and personalized medicine[J]. N Engl J Med, 2012, 366(10): 956-957.
|
| [19] |
Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis[J]. Eur Radiol Exp, 2018, 2(1): 36.
|
| [20] |
Pope WB, Qiao XJ, Kim HJ, et al. Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study[J]. J Neurooncol, 2012, 108(3): 491-498.
|
| [21] |
吴亚平，刘博，顾建钦,等.基于影像组学的脑胶质瘤分级方法[J].中华放射学杂志, 2017, 51(12): 902-905.
|
| [22] |
Hendriks EJ, Habets EJJ, Taphoorn MJB, et al. Linking late cognitive outcome with glioma surgery location using resection cavity maps[J]. Hum Brain Mapp, 2018, 39(5): 2064-2074.
|
| [23] |
Skogen K, Schulz A, Dormagen JB, et al. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas[J]. Eur J Radiol, 2016, 85(4): 824-829.
|
| [24] |
刘锐，赵旭，赵东利,等.基于MIM软件行CT-MRI图像融合技术在脑胶质瘤精确放疗中的应用[J].西安交通大学学报(医学版), 2020, 41(5): 668-672.
|
| [25] |
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-157.
|
| [26] |
Yip SS, Aerts HJ. Applications and limitations of radiomics[J]. Phys Med Biol, 2016, 61(13): R150-R166.
|
| [27] |
Robertson S, Azizpour H, Smith K, et al. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence[J]. Transl Res, 2018, 194: 19-35.
|
| [28] |
Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence[J]. Lancet Oncol, 2019, 20(5): e253-e261.
|
| [29] |
Rathore S, Niazi T, Iftikhar MA, et al. Glioma grading via analysis of digital pathology images using machine learning[J]. Cancers (Basel), 2020, 12(3): 578.
|
| [30] |
Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities[J]. J Pathol Inform, 2018, 9: 38.
|
| [31] |
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.
|
| [32] |
Shaver MM, Kohanteb PA, Chiou C, et al. Optimizing neuro-oncology imaging: a review of deep learning approaches for glioma imaging[J]. Cancers (Basel), 2019, 11(6): 829.
|
| [33] |
Sotoudeh H, Shafaat O, Bernstock JD, et al. Artificial intelligence in the management of glioma: era of personalized medicine[J]. Front Oncol, 2019, 9: 768.
|