Application of artificial intelligence in cancer research and clinical practice

doi:10.16352/j.issn.1001-6325.2022.11.1637

Abstract

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)

CLC Number:

R730.1

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.

References 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.