[1] Marusyk A, Almendro V, Polyak K.  Intra-tumour heterogeneity: a looking glass for cancer?[J]. Nat Rev Cancer, 2012, 12(5): 323-334.   doi: 10.1038/nrc3261
[2] 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.   doi: 10.1016/j.ejca.2011.11.036
[3] Kumar V, Gu YH, Basu S, et al.  Radiomics: the process and the challenges[J]. Magn Reson Imaging, 2012, 30(9): 1234-1248.   doi: 10.1016/j.mri.2012.06.010
[4] Doroshow JH, Kummar S.  Translational research in oncology — 10 years of progress and future prospects[J]. Nat Rev Clin Oncol, 2014, 11(11): 649-662.   doi: 10.1038/nrclinonc.2014.158
[5] Lambin P, Leijenaar RTH, Deist TM, et al.  Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762.   doi: 10.1038/nrclinonc.2017.141
[6] 闫梦梦, 王卫东, 郎锦义.  影像组学技术及其在肿瘤精准放疗中的应用[J]. 肿瘤预防与治疗, 2018, 31(5): 364-368.   doi: 10.3969/j.issn.1674-0904.2018.05.011
Yan MM, Wang WD, Lang JY.  Radiomics and its application in precision radiotherapy[J]. J Cancer Control Treat, 2018, 31(5): 364-368.   doi: 10.3969/j.issn.1674-0904.2018.05.011
[7] 张佳佳, 樊鑫, 秦珊珊, 等.  基于深度学习的人工智能在肿瘤诊断中的应用进展[J]. 国际放射医学核医学杂志, 2020, 44(1): 11-15.   doi: 10.3760/cma.j.issn.1673-4114.2020.01.004
Zhang JJ, Fan X, Qin SS, et al.  Advances in the application of artificial intelligence in cancer diagnosis and treatment[J]. Int Radiat Med Nucl Med, 2020, 44(1): 11-15.   doi: 10.3760/cma.j.issn.1673-4114.2020.01.004
[8] Kamiya A, Murayama S, Kamiya H, et al.  Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT[J]. Jpn J Radiol, 2014, 32(1): 14-21.   doi: 10.1007/s11604-013-0264-y
[9] Li ZJ, Mao Y, Li HS, et al.  Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR[J]. Magn Reson Med, 2016, 76(5): 1410-1419.   doi: 10.1002/mrm.26029
[10] Li ZJ, Mao Y, Huang W, et al.  Texture-based classification of different single liver lesion based on SPAIR T2W MRI images[J]. BMC Med Imaging, 2017, 17(1): 42-.   doi: 10.1186/s12880-017-0212-x
[11]

Liang CS, Huang YQ, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer[J/OL]. Oncotarget, 2016, 7(21): 31401−31412[2020-04-01]. https://www.oncotarget.com/article/8919/text. DOI: 10.18632/oncotarget.8919.

[12] Liu Y, Kim J, Balagurunathan Y, et al.  Radiomic features are associated with EGFR mutation status in lung adenocarcinomas[J]. Clin Lung Cancer, 2016, 17(5): 441-448.   doi: 10.1016/j.cllc.2016.02.001
[13]

Li ZJ, Wang YY, Yu JH, 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[2020-04-01]. https://www.nature.com/articles/s41598-017-05848-2. DOI: 10.1038/s41598-017-05848-2.

[14] Han L, Kamdar MR.  MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks[J]. Pac Symp on Biocomput, 2018, 23: 331-342.   doi: 10.1142/9789813235533_0031
[15] Thakkar JP, Dolecek TA, Horbinski C, et al.  Epidemiologic and molecular prognostic review of glioblastoma[J]. Cancer Epidemiol Biomarkers Prev, 2014, 23(10): 1985-1996.   doi: 10.1158/1055-9965.EPI-14-0275
[16] Feng M, Demiroz C, Vineberg K, et al.  Intra-observer variability of organs at risk for head and neck cancer: geometric and dosimetric consequences[J]. Int J Radiat Oncol Biol Phys, 2010, 78(3 Suppl): S444-445.   doi: 10.1016/j.ijrobp.2010.07.1044
[17]

Hardcastle N, Tomé WA, Cannon DM, et al. A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapy[J/OL]. Radiat Oncol, 2012, 7(1): 90[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3405479. DOI: 10.1186/1748-717X-7-90.

[18] Li XA, Tai A, Arthur DW, et al.  Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG multi-institutional and multiobserver study[J]. Int J Radiat Oncol Biol Phys, 2009, 73(3): 944-951.   doi: 10.1016/j.ijrobp.2008.10.034
[19] 周正东, 李剑波, 辛润超, 等.  基于带孔U-net神经网络的肺癌危及器官并行分割方法[J]. 东南大学学报: 自然科学版., 2019, 49(2): 231-236.   doi: 10.3969/j.issn.1001-0505.2019.02.005
Zhou ZD, Li JB, Xin RC, et al.  Parallel segmentation method for organs at risk in lung cancer based on dilated U-net neural network[J]. J Southeast Univ (Nat Sci Ed), 2019, 49(2): 231-236.   doi: 10.3969/j.issn.1001-0505.2019.02.005
[20]

Kazemifar S, Balagopal A, Nguyen D, et al. Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning[J/OL]. Biomed Phys Eng Express, 2018, 4(5): 055003[2020-04-01]. https://iopscience.iop.org/article/10.1088/2057-1976/aad100. DOI: 10.1088/2057-1976/aad100.

[21] Liang SJ, Tang F, Huang X, et al.  Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning[J]. Eur Radiol, 2019, 29(4): 1961-1967.   doi: 10.1007/s00330-018-5748-9
[22]

Trebeschi S, van Griethuysen JJM, Lambregts DMJ, et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR[J/OL]. Sci Rep, 2017, 7(1): 5301[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5509680. DOI: 10.1038/s41598-017-05728-9.

[23] Cardenas CE, McCarroll RE, Court LE, et al.  Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function[J]. Int J Radiat Oncol Biol Phys, 2018, 101(2): 468-478.   doi: 10.1016/j.ijrobp.2018.01.114
[24] Zhuge Y, Krauze AV, Ning H, et al.  Brain tumor segmentation using holistically nested neural networks in MRI images[J]. Med Phys, 2017, 44(10): 5234-5243.   doi: 10.1002/mp.12481
[25]

Men K, Chen XY, Zhang Y, et al. Deep deconvolutional neural network for target segmentation of nasopharyngeal cancer in planning computed tomography images[J/OL]. Front Oncol, 2017, 7: 315[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5770734. DOI: 10.3389/fonc.2017.00315.

[26] Men K, Zhang T, Chen XY, et al.  Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning[J]. Phys Med, 2018, 50: 13-9.   doi: 10.1016/j.ejmp.2018.05.006
[27]

Boon IS, Au Yong TPT, Boon C. Assessing the role of artificial intelligence (AI) in clinical oncology: utility of machine learning in radiotherapy target volume delineation[J/OL]. Medicines (Basel), 2018, 5(4): 131[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6313566. DOI: 10.3390/medicines5040131.

[28] Meyer P, Noblet V, Mazzara C, et al.  Survey on deep learning for radiotherapy[J]. Comput Biol Med, 2018, 98: 126-146.   doi: 10.1016/j.compbiomed.2018.05.018
[29]

Nguyen D, Long T, Jia X, et al. A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning[J/OL]. Sci Rep, 2019, 9(1): 1076[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355802. DOI: 10.1038/s41598-018-37741-x.

[30] Chen XY, Men K, Li YX, et al.  A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning[J]. Med Phys, 2019, 46(1): 56-64.   doi: 10.1002/mp.13262
[31] Fan JW, Wang JZ, Chen Z, et al.  Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique[J]. Med Phys, 2019, 46(1): 370-381.   doi: 10.1002/mp.13271
[32] Babier A, Mahmood R, McNiven AL, et al.  Knowledge-based automated planning with three-dimensional generative adversarial networks[J]. Med Phys, 2020, 47(2): 297-306.   doi: 10.1002/mp.13896
[33] Liu J, Mao Y, Li ZJ, et al.  Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma[J]. J Magn Reson Imaging, 2016, 44(2): 445-455.   doi: 10.1002/jmri.25156
[34] Cusumano D, Dinapoli N, Boldrini L, et al.  Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer[J]. Radiol Med, 2018, 123(4): 286-295.   doi: 10.1007/s11547-017-0838-3
[35]

Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer[J/OL]. Sci Rep, 2018, 8(1): 1−8[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105676. DOI: 10.1038/s41598-018-30657-6.

[36] Coroller TP, Agrawal V, Huynh E, et al.  Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC[J]. J Thorac Oncol, 2017, 12(3): 467-476.   doi: 10.1016/j.jtho.2016.11.2226
[37] Moran A, Daly ME, Yip SS, et al.  Radiomics-based assessment of radiation-induced lung injury after stereotactic body radiotherapy[J]. Clin Lung Cancer, 2017, 18(6): e425-e431.   doi: 10.1016/j.cllc.2017.05.014
[38] Ibragimov B, Toesca D, Chang D, et al.  Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT[J]. Med Phys, 2018, 45(10): 4763-4774.   doi: 10.1002/mp.13122
[39] Zhen X, Chen JW, Zhong ZC, et al.  Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study[J]. Phys Med Biol, 2017, 62(21): 8246-8263.   doi: 10.1088/1361-6560/aa8d09
[40] Zhang B, Tian J, Dong D, et al.  Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma[J]. Clin Cancer Res, 2017, 23(15): 4259-4269.   doi: 10.1158/1078-0432.CCR-16-2910
[41] van Timmeren JE, Leijenaar RTH, van Elmpt W, et al.  Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images[J]. Radiother Oncol, 2017, 123(3): 363-369.   doi: 10.1016/j.radonc.2017.04.016
[42]

Lao JW, Chen YS, Li ZC, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme[J/OL]. Sci Rep, 2017, 7(1): 10353[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583361. DOI: 10.1038/s41598-017-10649-8.

[43] Shen WC, Chen SW, Wu KC, et al.  Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography[J]. Eur Radiol, 2019, 29(12): 6741-6749.   doi: 10.1007/s00330-019-06265-x
[44] Dissaux G, Visvikis D, Da-Ano R, et al.  Pretreatment 18F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study[J]. J Nucl Med, 2020, 61(6): 814-820.   doi: 10.2967/jnumed.119.228106
[45] Cao Q, Li YM, Li Z, et al.  Development and validation of a radiomics signature on differentially expressed features of 18F-FDG PET to predict treatment response of concurrent chemoradiotherapy in thoracic esophagus squamous cell carcinoma[J]. Radiother Oncol, 2020, 146: 9-15.   doi: 10.1016/j.radonc.2020.01.027
[46]

He L, Huang YQ, Ma ZL, et al. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule[J/OL]. Sci Rep, 2016, 6: 34921[2020-04-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5056507. DOI: 10.1038/srep34921.

[47] Fave X, Cook M, Frederick A, et al.  Preliminary investigation into sources of uncertainty in quantitative imaging features[J]. Comput Med Imaging Graph, 2015, 44: 54-61.   doi: 10.1016/j.compmedimag.2015.04.006
[48] Yang JZ, Zhang LF, Fave XJ, et al.  Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors[J]. Comput Med Imaging Graph, 2016, 48: 1-8.   doi: 10.1016/j.compmedimag.2015.12.001