Visualization Analysis of Artificial Intelligence in Global Esophageal Cancer Research, 2000-2022

doi:10.12114/j.issn.1007-9572.2022.0461

Chinese General Practice ›› 2023, Vol. 26 ›› Issue (06): 760-768.DOI: 10.12114/j.issn.1007-9572.2022.0461

• Medical Information Research • Previous Articles Next Articles

Visualization Analysis of Artificial Intelligence in Global Esophageal Cancer Research, 2000-2022

1. Department of Epidemiology, School of Public Health, Nanchang University, Nanchang 330006, China
2. Department of Thoracic Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang 330006, China

Received:2022-05-20 Revised:2022-08-21 Published:2023-02-20 Online:2022-08-25
Contact: WU Lei
About author:
TU J X, YE H Q, ZHANG X Q, et al. Visualization analysis of artificial intelligence in global esophageal cancer research, 2000-2022[J]. Chinese General Practice, 2023, 26 (6) : 760-768.

2000—2022年人工智能应用于食管癌领域全球研究的可视化分析

1．330006　江西省南昌市，南昌大学公共卫生学院流行病学教研室
2．330006　江西省南昌市，南昌大学第二附属医院心胸外科

通讯作者: 吴磊
作者简介:
涂嘉欣，叶惠清，张小强，等. 2000—2022年人工智能应用于食管癌领域全球研究的可视化分析[J].中国全科医学，2023，26（6）：760-768. [www.chinagp.net] 作者贡献：涂嘉欣、吴磊提出研究设想及总体研究方案的构建，负责论文的撰写与修改，并对文章负责；叶惠清、林雪婷收集、清洗和保留研究数据（包括软件代码），以供研究使用和结果重现；张小强、吴磊为研究提供资金支持，对研究活动进行规划，执行的监督和领导，学科专业问题指导；杨善岚、邓莉芳对图片格式及文字修订，整理参考文献。
基金资助:
江西省城市癌症早诊早治疗经济学评价项目(JXTC2020040486); 2022年南昌大学大学生创新创业训练计划项目

Abstract

Abstract:

Background

The past nearly 20-year period has seen a sudden increase in the use of artificial intelligence (AI) in esophageal cancer research, and an emergence of many systematic reviews and meta-analyses of the research. However, most of the reviews and meta-analyses only address a single aspect in summary, making it difficult for researchers to gain a comprehensive understanding of the latest developments and research hotspots in the field.

Objective

To perform a bibliometric analysis of the use of AI in esophageal cancer research, and the development, hotspots and emerging trend in this field.

Methods

All literature in English regarding esophageal cancer research using AI included in the Science Citation Index Expanded database of the Web of Science Core Collection was searched from 2000-01-01 to 2022-04-06. Microsoft Excel 2019, CiteSpace (5.8R3-64bit) and VOSviewer (1.6.18) were used to analyze the literature for annual number of publications, country, author, institution, co-citation and keywords.

Results

Nine hundred and eighteen studies were retrieved, with a total of 23 490 times of being cited. The number of studies published between 2000 and 2016 grew slowly (from 6 to 40), but increased rapidly between 2017 and 2022 (from 62 to 216). Sixty countries, 118 institutions and 5 979 authors were involved in the studies. China (306 articles), the United States (238 articles) and the United Kingdom (113 articles) ranked the top three in terms of number of studies published. The top three institutions in terms of intensity of cooperation were University of Amsterdam (TLS=72), Catherine Hospital (TLS=64) and Eindhoven University of Technology (TLS=53). The top three authors in terms of number of publications were Jacques J G H M Bergman from the Netherlands (n=16), Tomohiro Tada from Japan (n=12), and Fons Van Der Sommen from the Netherlands (n=12). There were 39 962 co-cited authors and 42 992 co-cited studies. Thirty-three burst keywords were identified: the major burst keywords were p53 and mutations in 2001-2008 (early stage), and were esophageal cancer classification, new examination techniques (tomography), differentiation, identification and comparison between esophageal cancer and other cancers in 2013-2018 (middle stage), and were deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis applications in 2019-2022 (late stage). Among which deep learning had the highest burst intensity (burst intensity of 13.89) .

Conclusion

AI application in esophageal cancer research has entered a new phase, moving gradually from genes and mutations toward accurate examination, diagnosis, and treatment. The latest major burst keywords in recent years (2019-2022) are deep learning, convolutional neural network, and machine learning in esophageal cancer examination and diagnosis. The future challenges to the use of AI in esophageal cancer research may include individual data collection, data quality assurance, data processing specifications, AI code reproduction, and reliability assurance of AI-assisted diagnostic decision-making.

Key words: Esophageal neoplasms, Artificial intelligence, Data collection, Diagnosis, differential, Visual analysis, CiteSpace

摘要：

背景

随着近20余年人工智能（AI）在食管癌领域应用研究的骤增，出现了许多关于该研究的系统、荟萃分析等，但其仅针对AI在该领域应用的单一方面的总结研究，研究人员难以全面了解领域最新发展与研究热点。

目的

通过文献计量分析总结AI在食管癌领域的应用，阐明AI在食管癌领域相关研究的进展、热点和新兴趋势。

方法

检索Web of Science Core Collection（WoSCC）的Science Citation Index Expanded（SCI-E）数据库收录的AI应用于食管癌领域的所有英文文献，检索时间2000-01-01至2022-04-06。应用Microsoft Excel 2019、CiteSpace（5.8R3-64bit）和VOSviewer（1.6.18）对文献进行发文量、国家、作者、机构、共被引和关键词分析。

结果

2000—2022年共检索到AI应用于食管癌领域的文献918篇，共计引用文献总量23 490篇。发文趋势：2000—2016年为迟缓期，发文量从6篇增至40篇；2017—2022年为快速增长期，发文量从62篇突增至216篇。60个国家、118家机构、5 979位作者参与了AI在食管癌领域应用的研究，发文量排名前3位的国家分别是中国（306篇）、美国（238篇）、英国（113篇），机构合作强度排名前3位的分别是阿姆斯特丹大学〔连线粗细（TLS）=72〕、凯瑟琳娜医院（TLS=64）、埃因霍芬大学（TLS=53），发文量排名前3位的作者是荷兰的作者Jacques J G H M Bergman（16篇）、日本的作者Tomohiro Tada（12篇）、荷兰的作者Fons Van Der Sommen（12篇）。共被引作者39 962位，共被引文献42 992篇。AI应用于食管癌领域相关研究的突现关键词共33个，早期（2001—2008年）：突现关键词以p53、突变为主；中期（2013—2018年）：以食管癌分类、检查新技术（断层扫描）以及食管癌和不同癌症之间区分、鉴别和比较为主；近期（2019—2022年）：以深度学习、卷积神经网络、机器学习在食管癌检查、诊断应用为最新前沿，且深度学习一词突现强度排在33个突现关键词首位（突现强度为13.89）。

结论

AI在食管癌领域的相关研究已迈入新阶段，从基因、突变逐步朝精准检查、诊断和治疗方向发展，深度学习、卷积神经网络、机器学习在食管癌检查、诊断应用为近期（2019—2022年）AI应用于食管癌领域的最新前沿。未来AI应用于食管癌的挑战可能主要集中在食管癌个体化数据收集、数据质量、数据处理规范、AI代码复现、辅助诊断可信度决策上。

关键词: 食管癌, 人工智能, 数据收集, 诊断，鉴别, 可视化分析, CiteSpace

TU Jiaxin,YE Huiqing,ZHANG Xiaoqiang, et al. Visualization Analysis of Artificial Intelligence in Global Esophageal Cancer Research, 2000-2022[J]. Chinese General Practice, 2023, 26(06): 760-768. DOI: 10.12114/j.issn.1007-9572.2022.0461.
涂嘉欣,叶惠清,张小强等. 2000—2022年人工智能应用于食管癌领域全球研究的可视化分析[J]. 中国全科医学, 2023, 26(06): 760-768. DOI: 10.12114/j.issn.1007-9572.2022.0461.

Figures/Tables 15

References 20

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检索步骤	检索式	文献数量（篇）
1#	esophag* （Topic） or oesophag* （Topic） or gullet （Topic） and Article OR Review （Document Type） and English （Language）	103 423
2#	cancer* （Topic） or tumour* （Topic） or tumor* （Topic） or neoplas* （Topic） or onco* （Topic） or carcinoma* （Topic） and Article OR Review （Document Type） and English （Language）	3 272 272
3#	1# AND 2#	54 077
4#	"artificial intelligen" （Topic） or computational NEAR/5 intelligence （Topic） or expert system* （Topic） or intelligent learning （Topic） or feature* extraction （Topic） or feature* mining （Topic） or feature* learning （Topic） or machine learning （Topic） or feature* selection （Topic） or unsupervised clustering （Topic） or image* segmentation （Topic） or supervised learning （Topic） or semantic segmentation （Topic） or deep network* （Topic） or bayes* network （Topic） or deep learning （Topic） or neural network* （Topic） or neural learning （Topic） or neural nets model （Topic） or artificial neural network （Topic） or data mining （Topic） or graph mining （Topic） or data clustering （Topic） or big data （Topic） or knowledge graph （Topic） or AI （Topic） and Article OR Review （Document Type） and English （Language）	1 068 667
5#	3# AND 4#	1 074

检索步骤	检索式	文献数量（篇）
1#	esophag* （Topic） or oesophag* （Topic） or gullet （Topic） and Article OR Review （Document Type） and English （Language）	103 423
2#	cancer* （Topic） or tumour* （Topic） or tumor* （Topic） or neoplas* （Topic） or onco* （Topic） or carcinoma* （Topic） and Article OR Review （Document Type） and English （Language）	3 272 272
3#	1# AND 2#	54 077
4#	"artificial intelligen" （Topic） or computational NEAR/5 intelligence （Topic） or expert system* （Topic） or intelligent learning （Topic） or feature* extraction （Topic） or feature* mining （Topic） or feature* learning （Topic） or machine learning （Topic） or feature* selection （Topic） or unsupervised clustering （Topic） or image* segmentation （Topic） or supervised learning （Topic） or semantic segmentation （Topic） or deep network* （Topic） or bayes* network （Topic） or deep learning （Topic） or neural network* （Topic） or neural learning （Topic） or neural nets model （Topic） or artificial neural network （Topic） or data mining （Topic） or graph mining （Topic） or data clustering （Topic） or big data （Topic） or knowledge graph （Topic） or AI （Topic） and Article OR Review （Document Type） and English （Language）	1 068 667
5#	3# AND 4#	1 074

序号	共被引作者	地区	共被引次数（次）	总被引次数（次）	中介中心性
1	Freddie Ian Bray	法国	89	304	0
2	Prateek Sharma	美国	87	1 907	0.04
3	Yoshimasa Horie	日本	56	691	0.04
4	Jacques Ferlay	法国	53	748	0.01
5	Jesper Lagergren	瑞典	52	1 591	0.15
6	Lambin Philippe	比利时	46	562	0.04
7	Rebecca L Siegel	美国	46	417	0
8	Hirasawa Toshiaki	日本	45	1 025	0.06
9	Nicholas James Shaheen	美国	45	136	0.04
10	Thomas William Rice	美国	43	1 107	0.12

序号	共被引作者	地区	共被引次数（次）	总被引次数（次）	中介中心性
1	Freddie Ian Bray	法国	89	304	0
2	Prateek Sharma	美国	87	1 907	0.04
3	Yoshimasa Horie	日本	56	691	0.04
4	Jacques Ferlay	法国	53	748	0.01
5	Jesper Lagergren	瑞典	52	1 591	0.15
6	Lambin Philippe	比利时	46	562	0.04
7	Rebecca L Siegel	美国	46	417	0
8	Hirasawa Toshiaki	日本	45	1 025	0.06
9	Nicholas James Shaheen	美国	45	136	0.04
10	Thomas William Rice	美国	43	1 107	0.12

序号	文章题目	期刊名称	第一作者	出版年份（年）	共被引次数（次）
1	Global cancer statistics 2018：Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries	Ca-A Cancer Journal for Clinicians	BRAY	2018	87
2	Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks	Gastrointestinal Endoscopy	HORIE	2019	56
3	Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images	Gastric Cancer	HIRASAWA	2018	43
4	Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model （with videos）	Gastrointestinal Endoscopy	GUO	2019	37
5	Deep-learning system detects neoplasia in patients with barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking	Gastroenterology	DE GROOF	2020	32
6	Endoscopic detection and differentiation of esophageal lesions using a deep neural network	Gastrointestinal Endoscopy	OHMORI	2020	32
7	Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma （with video）	Gastrointestinal Endoscopy	CAI	2019	31
8	Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy	Endoscopy	ZHAO	2019	30
9	Integrated genomic characterization of oesophageal carcinoma	Nature	KIM	2017	30
10	Cancer statistics in China，2015	Ca-A Cancer Journal for Clinicians	CHEN	2016	30

Visualization Analysis of Artificial Intelligence in Global Esophageal Cancer Research, 2000-2022

2000—2022年人工智能应用于食管癌领域全球研究的可视化分析

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序号	中心性	频率	年份（年）	关键词
1	0.31	16	2000	结直肠癌
2	0.23	71	2000	癌症
3	0.23	41	2000	上皮小细胞癌
4	0.19	49	2000	小细胞癌
5	0.19	6	2005	突变
6	0.18	47	2003	巴雷特食管癌
7	0.17	48	2000	食管癌
8	0.17	21	2001	乳腺癌
9	0.17	6	2001	p53
10	0.13	25	2001	上皮组织癌
11	0.12	10	2003	肿瘤
12	0.11	23	2007	风险
13	0.11	8	2015	食管腺癌

序号	中心性	频率	年份（年）	关键词	序号	中心性	频率	年份（年）	关键词
1	0.24	11	2017	生物学标志物	14	0.14	20	2017	验证
2	0.22	18	2017	基因	15	0.13	36	2017	上皮组织癌
3	0.20	38	2017	表达	16	0.13	4	2020	计算机辅助诊断
4	0.19	19	2017	发育不良	17	0.13	26	2017	肺癌
5	0.18	15	2017	协助	18	0.13	13	2017	食管癌
6	0.18	6	2020	计算机辅助检测	19	0.13	2	2020	CPR
7	0.17	8	2019	准确度	20	0.13	4	2018	术前放化疗
8	0.17	10	2017	氟-¹⁸FDG断层扫描	21	0.12	4	2020	食管上皮小细胞癌
9	0.17	4	2017	肿瘤抑制因子	22	0.11	5	2021	生物信息学分析
10	0.16	11	2017	数据	23	0.11	42	2017	CT检查
11	0.15	22	2017	结肠癌	24	0.11	4	2018	标志物
12	0.14	6	2017	FDG断层扫描	25	0.11	41	2017	风险
13	0.14	11	2017	鉴别	26	0.11	2	2017	纹理特征

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