| [1] |
Chen W, Zheng R, Baade PD, et al. Cancer statistics in china, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132.
|
| [2] |
Torre L A, Bray F, Siegel RL, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65(2): 87-108.
|
| [3] |
Lagergren J, Smyth E, Cunningham D, et al. Oesophageal cancer[J]. Lancet, 2017, 390(10110): 2383-2396.
|
| [4] |
Zeng H, Chen W, Zheng R, et al. Changing cancer survival in china during 2003-15: a pooled analysis of 17 population-based cancer registries[J]. The Lancet Global health, 2018, 6(5): e555-e567.
|
| [5] |
Geetha R, Bhanu Sree, Reddy D. Recruitment through artificial intelligence: a conceptual study[J]. International Journal of Mechanical Engineering and Technology, 2018, 9(7): 63-70.
|
| [6] |
Stevan H. The symbol grounding problem[J]. Physica D: Nonlinear Phenomena, 1990, 42(1): 335-346.
|
| [7] |
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316(22): 2402-2410.
|
| [8] |
Zhang S, Song G, Zang Y, et al. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery[J]. Eur Radiol, 2018, 28(9): 3692-3701.
|
| [9] |
Lu MY, Chen TY, Williamson DFK, et al. Ai-based pathology predicts origins for cancers of unknown primary[J]. Nature, 2021, 594(7861): 106-110.
|
| [10] |
Sullivan P, Gupta S, Powers PD, et al. Artificial intelligence research and development for application in video capsule endoscopy[J]. Gastrointest Endosc Clin N Am, 2021, 31(2): 387-397.
|
| [11] |
Liu Z, Zhang XY, Shi YJ, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer[J]. Clin Cancer Res, 2017, 23(23): 7253-7262.
|
| [12] |
Sun C, Tian X, Liu Z, et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: a multicentre study[J]. EBioMedicine, 2019, 46: 160-169.
|
| [13] |
Chaudhary K, Poirion OB, Lu L, et al. Deep learning-based multi-omics integration robustly predicts survival in liver cancer[J]. Clin Cancer Res, 2018, 24(6): 1248-1259.
|
| [14] |
Kim H, Goo JM, Lee KH, et al. Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas[J]. Radiology, 2020, 296(1): 216-224.
|
| [15] |
Tian J,Dong D, Liu ZY, et al. Radiomics and its clinical application artificial intelligence and medical big data[M]. London, United Kingdom: Academic Press, 2021: 6-7.
|
| [16] |
Codipilly DC, Qin Y, Dawsey SM, et al. Screening for esophageal squamous cell carcinoma: recent advances[J]. Gastrointest Endosc, 2018, 88(3): 413-426.
|
| [17] |
National Comprehensive Cancer Network. NCCN clinical practice guidelines: esophageal and esophagogastric junction cancers[R]. Accessed 01 May 2021）
URL
|
| [18] |
董志伟. 中国癌症筛查及早诊早治指南[M]. 北京: 北京大学医学出版社, 2005: 47-52.
|
| [19] |
Frazzoni L, Arribas J, Antonelli G, et al. Endoscopist diagnostic accuracy in detecting upper-gi neoplasia in the framework of artificial intelligence studies[J]. Endoscopy, 2021. DOI: 10.1055/a-1500-3730. Epub ahead of print. |
| [20] |
Domper Arnal MJ, Ferrández Arenas á, Lanas Arbeloa á. Esophageal cancer: Risk factors, screening and endoscopic treatment in western and eastern countries[J]. World J Gastroenterol, 2015, 21(26): 7933-7943.
|
| [21] |
Ishihara R, Takeuchi Y, Chatani R, et al. Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists[J]. Dis Esophagus, 2010, 23(6): 480-486.
|
| [22] |
Ide H, Nakamura T, Hayashi K, et al. Esophageal squamous cell carcinoma: pathology and prognosis[J]. World J Surg, 1994, 18(3): 321-330.
|
| [23] |
Choi SW, Cho HH, Koo H, et al. Multi-habitat radiomics unravels distinct phenotypic subtypes of glioblastoma with clinical and genomic significance[J]. Cancers (Basel), 2020, 12(7): 1707.
|
| [24] |
Chang X, Deng W, Ni W, et al. Comparison of two major staging systems in predicting survival and recommendation of postoperative radiotherapy based on the 11th Japanese classification for esophageal carcinoma after curative resection: a propensity score-matched analysis[J]. Ann Surg Oncol, 2021. DOI: 10.1245/s10434-021-10046-6. Epub ahead of print. |
| [25] |
中国医师协会消化内镜人工智能专业委员会，上海市计算技术研究所上海市医疗器械检测所. 消化内镜人工智能数据采集与标注质量控制体系专家共识意见(草案2019,上海)[J]. 中华消化内镜杂志, 2020, 37(8): 533-539.
|
| [26] |
Kurokawa T, Hamai Y, Emi M, et al. Risk factors for recurrence in esophageal squamous cell carcinoma without pathological complete response after trimodal therapy[J]. Anticancer Res, 2020, 40(8): 4387-4394.
|
| [27] |
Kim H, Park MS, Choi JY, et al. Can microvessel invasion of hepatocellular carcinoma be predicted by pre-operative MRI?[J]. Eur Radiol, 2009, 19(7): 1744-1751.
|
| [28] |
中华医学会放射学分会医学影像大数据与人工智能工作委员会，中华医学会放射学分会磁共振学组. 结直肠癌CT和MRI标注专家共识[J]. 中华放射学杂志, 2021, 55(2): 111-116.
|
| [29] |
Qu J, Shen C, Qin J, et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer[J]. Eur Radiol, 2019, 29(2): 906-914.
|
| [30] |
Hou Z, Li S, Ren W, et al. Radiomic analysis in T2W and spair T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma[J]. J Thorac Dis, 2018, 10(4): 2256-2267.
|
| [31] |
Zhang F, Zhong LZ, Zhao X, et al. A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study[J]. Ther Adv Med Oncol, 2020, 12:1758835920971416.
|
| [32] |
Kather JN, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study[J]. PLoS Med, 2019, 16(1): e1002730.
|
| [33] |
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 5: 4006.
|
| [34] |
Xie CY, Pang CL, Chan B, et al. Machine learning and radiomics applications in esophageal cancers using non-invasive imaging methods-a critical review of literature[J]. Cancers (Basel), 2021, 13(10): 2469.
|
| [35] |
Orlhac F, Frouin F, Nioche C, et al. Validation of a method to compensate multicenter effects affecting CT radiomics[J]. Radiology, 2019, 291(1): 53-59.
|
| [36] |
Kan R, Zhang W, Ke C, et al. Bidding machine: learning to bid for directly optimizing profits in display advertising[J]. IEEE T Knowl Data En, 2018, 30(99): 645-659.
|
| [37] |
Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE T Pattern Anal, 2005, 27(8): 1226-1238.
|
| [38] |
Wang G, Zuluaga MA, Li W, et al. Deepigeos: a deep interactive geodesic framework for medical image segmentation[J]. IEEE T Pattern Anal, 2019, 41(7): 1559-1572.
|
| [39] |
He K, Gkioxari G, Dollar P, et al. Mask R-CNN[J]. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 386-397.
|
| [40] |
Danelljan M, Hager G, Khan FS, et al. Discriminative scale space tracking[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(8): 1561-1575.
|
| [41] |
Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks[J]. J Med Imaging (Bellingham, Wash), 2016, 3(3): 034501.
|
| [42] |
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
|
| [43] |
Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks[J]. Gastrointest Endosc, 2019, 89(1): 25-32.
|
| [44] |
Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network[J]. Gastrointest Endosc, 2020, 91(2): 301-309.e1.
|
| [45] |
Fukuda H, Ishihara R, Kato Y, et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video)[J]. Gastrointest Endosc, 2020, 92(4): 848-855.
|
| [46] |
Waki K, Ishihara R, Kato Y, et al. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation[J]. Dig Endosc, 2021. DOI: 10.1111/den.13934. Epub ahead of print. |
| [47] |
Inoue H, Kaga M, Ikeda H, et al. Magnification endoscopy in esophageal squamous cell carcinoma: a review of the intrapapillary capillary loop classification[J]. Ann Gastroenterol, 2015, 28(1): 41-48.
|
| [48] |
Everson M, Herrera L, Li W, et al. Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: a proof-of-concept study[J]. Eur J Gastroenterol Hepatol, 2019, 7(2): 297-306.
|
| [49] |
Kumagai Y, Takubo K, Kawada K, et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus[J]. Esophagus, 2019, 16(2): 180-187.
|
| [50] |
Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy:a multicentre, case-control, diagnostic study[J]. Lancet Oncol, 2019, 20(12): 1645-1654.
|
| [51] |
Hussein M, Everson M, Haidry R. Esophageal squamous dysplasia and cancer: is artificial intelligence our best weapon?[J]. Best Pract Res Clin Gastroenterol, 2021, 52-53: 101723.
|
| [52] |
Piccirelli S, Milluzzo SM, Bizzotto A, et al. Small bowel capsule endoscopy and artificial intelligence: first or second reader?[J]. Best Pract Res Clin Gastroenterol, 2021, 52-53:101742.
|
| [53] |
中国临床肿瘤学会指南工作委员会. 中国临床肿瘤学会(CSCO)食管癌诊疗指南2021[M]. 北京: 人民卫生出版社, 2020: 43.
|
| [54] |
Ebi M, Shimura T, Yamada T, et al. Multicenter, prospective trial of white-light imaging alone versus white-light imaging followed by magnifying endoscopy with narrow-band imaging for the real-time imaging and diagnosis of invasion depth in superficial esophageal squamous cell carcinoma[J]. Gastrointest Endosc, 2015, 81(6): 1355-1361. e2.
|
| [55] |
Thosani N, Singh H, Kapadia A, et al. Diagnostic accuracy of eus in differentiating mucosal versus submucosal invasion of superficial esophageal cancers: a systematic review and meta-analysis[J]. Gastrointest Endosc, 2012, 75(2): 242-253.
|
| [56] |
Nakagawa K, Ishihara R, Aoyama K, et al. Classification for invasion depth of esophageal squamous cell carcinoma usinga deep neural network compared with experienced endoscopists[J]. Gastrointest Endosc, 2019, 90(3): 407-414.
|
| [57] |
Shimamoto Y, Ishihara R, Kato Y, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence[J]. J Gastroenterol, 2020, 55(11): 1037-1045.
|
| [58] |
Zhou Y, Du J, Wang Y, et al. Prediction of lymph node metastatic status in superficial esophageal squamous cell carcinoma using an assessment model combining clinical characteristics and pathologic results: a retrospective cohort study[J]. Int J Surg, 2019, 66: 53-61.
|
| [59] |
Zheng H, Tang H, Wang H, et al. Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma[J]. Br J Surg, 2018, 105(11): 1464-1470.
|
| [60] |
Min BH, Yang JW, Min YW, et al. Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma[J]. J Gastroenterol Hepatol, 2020, 35(6): 1009-1015.
|
| [61] |
Cao Q, Li Y, Li Z, et al. Development and validation of a radiomics signature on differentially expressed features of (18)F-FDG PET to predict treatment response of concurrent chemoradiotherapy in thoracic esophagus squamous cell carcinoma[J]. Radiother Oncol, 2020, 146: 9-15.
|
| [62] |
Zhang C, Shi Z, Kalendralis P, et al. Prediction of lymph node metastases using pre-treatment pet radiomics of the primary tumour in esophageal adenocarcinoma: an external validation study[J]. Br J Radiol, 2021, 94(1118): 20201042.
|
| [63] |
Chen H, Zhou X, Tang X, et al. Prediction of lymph node metastasis in superficial esophageal cancer using a pattern recognition neural network[J]. Cancer Manag Res, 2020, 12: 12249-12258.
|
| [64] |
Wu L, Yang X, Cao W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: a multicentre retrospective study[J]. Front Oncol, 2019, 9: 1548.
|
| [65] |
Zhu H, Zhang X, Li X, et al. Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy[J]. Chin J Cancer Res, 2019, 31(6): 984-992.
|
| [66] |
Jin X, Zheng X, Chen D, et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics[J]. Eur Radiol, 2019, 29(11): 6080-6088.
|
| [67] |
Xie C, Yang P, Zhang X, et al. Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy[J]. EBioMedicine, 2019, 44: 289-297.
|
| [68] |
Hu Y, Xie C, Yang H, et al. Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma[J]. Radiother Oncol, 2021, 154: 6-13.
|
| [69] |
Javeri H, Xiao L, Rohren E, et al. Influence of the baseline 18F-fluoro-2-deoxy-d-glucose positron emission tomography results on survival and pathologic response in patients with gastroesophageal cancer undergoing chemoradiation[J]. Cancer, 2009, 115(3): 624-630.
|
| [70] |
Ganeshan B, Skogen K, Pressney I, et al. Tumour heterogeneity in oesophageal cancer assessed by ct texture analysis: Preliminary evidence of an association with tumour metabolism, stage, and survival[J]. Clin Radiol, 2012, 67(2): 157-164.
|
| [71] |
Kukar M, Alnaji RM, Jabi F, et al. Role of repeat 18F-fluorodeoxyglucose positron emission tomography examination in predicting pathologic response following neoadjuvant chemoradiotherapy for esophageal adenocarcinoma[J]. JAMA Surg, 2015, 150(6): 555-562.
|
| [72] |
Ypsilantis PP, Siddique M, Sohn HM, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks[J]. PLoS One, 2015, 10(9): e0137036.
|
| [73] |
Xie CY, Hu YH, Ho JW, et al. Using genomics feature selection method in radiomics pipeline improves prognostication performance in locally advanced esophageal squamous cell carcinoma-a pilot study[J]. Cancers (Basel), 2021, 13(9): 2145
|
| [74] |
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318(22): 2199-2210.
|
| [75] |
Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks[J]. Proc Natl Acad Sci U S A, 2018, 115(13): E2970-E2979.
|
| [76] |
Guo YN, Tian DP, Gong QY, et al. Perineural invasion is a better prognostic indicator than lymphovascular invasion and a potential adjuvant therapy indicator for pN0M0 esophageal squamous cell carcinoma[J]. Ann Surg Oncol, 2020, 27(11): 4371-4381.
|
| [77] |
Wang C, Wang J, Chen Z, et al. Immunohistochemical prognostic markers of esophageal squamous cell carcinoma: a systematic review[J]. Chin J Cancer, 2017, 36(1): 65.
|
| [78] |
Mourikis TP, Benedetti L, Foxall E, et al. Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma[J]. Nat Commun, 2019, 10(1): 3101.
|
| [79] |
Xu Y, Selaru FM, Yin J, et al. Artificial neural networks and gene filtering distinguish between global gene expression profiles of barrett's esophagus and esophageal cancer[J]. Cancer Res, 2002, 62(12): 3493-3497.
|
| [80] |
Kan T, Shimada Y, Sato F, et al. Prediction of lymph node metastasis with use of artificial neural networks based on gene expression profiles in esophageal squamous cell carcinoma[J]. Ann Surg Oncol, 2004, 11(12): 1070-1078.
|