人工智能在肿瘤研究和临床中的应用

doi:10.16352/j.issn.1001-6325.2022.11.1637

基础医学与临床 ›› 2022, Vol. 42 ›› Issue (11): 1637-1643.doi: 10.16352/j.issn.1001-6325.2022.11.1637

• 特邀专题：人工智能在医学中的应用 • 下一篇

人工智能在肿瘤研究和临床中的应用

陈明扬, 蔡紫庭, 薛鹏, 江宇^*, 乔友林^*

北京协和医学院群医学及公共卫生学院,北京 100005

收稿日期:2022-07-15 修回日期:2022-08-23 出版日期:2022-11-05 发布日期:2022-11-01
通讯作者: ^* jiangyu@pumc.edu.cn; qiaoy@cicams.ac.cn
基金资助:
中国医学科学院医学与健康科技创新工程项目(2021-I2M-1-004)

Application of artificial intelligence in cancer research and clinical practice

CHEN Ming-yang, CAI Zi-ting, XUE Peng, JIANG Yu^*, QIAO You-lin^*

School of Population Medicine and Public Health, Peking Union Medical College, Beijing 100005, China

Received:2022-07-15 Revised:2022-08-23 Online:2022-11-05 Published:2022-11-01
Contact: ^* jiangyu@pumc.edu.cn; qiaoy@cicams.ac.cn

摘要/Abstract

摘要： 人工智能(AI)的快速发展为肿瘤防控带来新的机遇。本综述首先简要概述了AI的起源和基本要素,接着梳理了AI在肿瘤领域的获批产品,并围绕基于图像的肿瘤筛查和诊断,列举了AI在几大肿瘤中的重要研究进展。最后,从数据、算法、应用和伦理角度出发,总结了AI在肿瘤应用中的问题和展望,为未来肿瘤领域的AI研究提供更多思路。

关键词: 人工智能, 肿瘤防控, 机器学习, 深度学习

Abstract: The rapid development of artificial intelligence (AI) has brought new opportunities for cancer prevention and control. This review give a brief overview of the origin and basic elements of AI. Then it comprehensively carded the application of AI systems in the field of cancer, reviewed the important research progress of AI in several major types of cancer around image-based cancer screening and diagnosis. Finally, this review summarized the challenges in algorithm, implementation and ethics and future development as well as more ideas for the application of AI technology in the oncological research.

Key words: artificial intelligence (AI), cancer prevention and control, machine learning (ML), deep learning (DL)

中图分类号:

R730.1

陈明扬, 蔡紫庭, 薛鹏, 江宇, 乔友林. 人工智能在肿瘤研究和临床中的应用[J]. 基础医学与临床, 2022, 42(11): 1637-1643.

CHEN Ming-yang, CAI Zi-ting, XUE Peng, JIANG Yu, QIAO You-lin. Application of artificial intelligence in cancer research and clinical practice[J]. Basic & Clinical Medicine, 2022, 42(11): 1637-1643.

参考文献 28

[1]	Turing AM. Computing machinery and intelligence[J]. Mind, 1950, LIX: 433-460.
[2]	Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8: 2328-2331.
[3]	金昱, 潘凯枫, 张艺宝, 等. 人工智能在肿瘤三级预防中的应用、机遇与挑战[J]. 中国肿瘤临床, 2021, 48: 1082-1087.
[4]	Shimizu H, Nakayama KI. Artificial intelligence in oncology[J]. Cancer Sci, 2020, 111: 1452-1460.
[5]	Kann BH, Hosny A, Aerts H. Artificial intelligence for clinical oncology[J]. Cancer Cell, 2021, 39: 916-927.
[6]	Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69: 127-157.
[7]	Elemento O, Leslie C, Lundin J, et al. Artificial intelligence in cancer research, diagnosis and therapy[J]. Nat Rev Cancer, 2021, 21: 747-752.
[8]	Freeman K, Geppert J, Stinton C, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy[J]. BMJ, 2021, 374: n1872.
[9]	Taylor-Phillips S, Seedat F, Kijauskaite G, et al. UK National Screening Committee's approach to reviewing evidence on artificial intelligence in breast cancer screen-ing[J]. Lancet Digit Health, 2022, 4: e558-e565.
[10]	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learn-ing on low-dose chest computed tomography[J]. Nat Med, 2019, 25: 954-961.
[11]	Hassan C, Spadaccini M, Iannone A, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis[J]. Gastrointest Endosc, 2021, 93: 77-85.
[12]	Jones OT, Matin RN, van der Schaar M, et al. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review[J]. Lancet Digit Health, 2022, 4: e466-e476.
[13]	Xue P, Wang J, Qin D, et al. Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis[J]. NPJ Digit Med, 2022, 5: 19.
[14]	Xue P, Tang C, Li Q, et al. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies[J]. BMC Med, 2020, 18: 406.
[15]	Wen D, Khan SM, Ji Xu A, et al. Characteristics of publicly available skin cancer image datasets: a systematic review[J]. Lancet Digit Health, 2022, 4: e64-e74.
[16]	Kaushal A, Altman R, Langlotz C. Geographic distribu-tion of US cohorts used to train deep learning algorithms[J]. JAMA, 2020, 324: 1212-1213.
[17]	Khan SM, Liu X, Nath S, et al. A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability[J]. Lancet Digit Health, 2021, 3: e51-e66.
[18]	Ibrahim H, Liu X, Zariffa N, et al. Health data poverty: an assailable barrier to equitable digital health care[J]. Lancet Digit Health, 2021, 3: e260-e265.
[19]	London JW. Cancer research data-sharing networks[J]. JCO Clin Cancer Inform, 2018, 2: 1-3.
[20]	孔祥维, 唐鑫泽, 王子明. 人工智能决策可解释性的研究综述[J]. 系统工程理论与实践, 2021, 41: 524-536.
[21]	Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group[J]. Nat Med, 2020, 26: 807-808.
[22]	Shelmerdine SC, Arthurs OJ, Denniston A, et al. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare[J]. BMJ Health Care Inform, 2021, 28: e100385.
[23]	Ibrahim H, Liu X, Rivera SC, et al. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines[J]. Trials, 2021, 22: 11.
[24]	Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension[J]. Nat Med, 2020, 26: 1364-1374.
[25]	Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence[J]. BMJ Open, 2021, 11: e048008.
[26]	Walter FM, Thompson MJ, Wellwood I, et al. Evaluating diagnostic strategies for early detection of cancer: the CanTest framework[J]. BMC Cancer, 2019, 19: 586.
[27]	Kleppe A, Skrede OJ, De Raedt S, et al. Designing deep learning studies in cancer diagnostics[J]. Nat Rev Cancer, 2021, 21: 199-211.
[28]	WHO. Ethics and governance of artificial intelligence for health: WHO guidance executive summary [EB/OL]. (2021-06-28). [2022-06-25]https://www.who.int/publications/i/item/9789240037403.

人工智能在肿瘤研究和临床中的应用

Application of artificial intelligence in cancer research and clinical practice

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