Progress of artificial intelligence application in cancer diagnosis and treatment

doi:10.16352/j.issn.1001-6325.2022.08.1297

Abstract

Abstract: The artificial intelligence (AI) technology with supreme capacity of big data integration and management have been used in digital diagnosis and clinical treatment,which have greatly facilitated the development of clinical oncology and precision medicine. AI technology is far superior to human beings in big data integration and analysis, so has been widely applied in pathological image analysis, molecular marker discovery, clinical decision support, drug discovery and recommendation and so on. In recent years, many remarkable achievements have been made in the field. This review discussed the latest applications of AI-assisted precision cancer diagnosis and treatment, highlighted emerging challenges, and predicted the development trend in the future.

Key words: artificial intelligence, cancer diagnosis, deep learning, precision medicine

CLC Number:

R446.9

DU Ming-yue, LI Xue-guang, ZUO Shan-ru, CHEN Zi-qi, ZHOU Jun-hua, LI Hao, HE Quan-yuan. Progress of artificial intelligence application in cancer diagnosis and treatment[J]. Basic & Clinical Medicine, 2022, 42(8): 1297-1301.

References 23

[1]	Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542:115-118.
[2]	Yoo H, Kim KH, Singh R, et al. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs[J]. JAMA Netw Open, 2020, 3:e2017135. doi:10.1001/jamanetworkopen.2020.17135.
[3]	Lu MY, Chen TY, Williamson DFK, et al. AI-based pathology predicts origins for cancers of unknown primary[J]. Nature, 2021, 594:106-110.
[4]	Tahmassebi A, Wengert GJ, Helbich TH, et al. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients[J]. Invest Radiol, 2019, 54:110-117.
[5]	王培培,吴斌.人工智能技术在结直肠癌全程管理中的应用[J].基础医学与临床, 2020, 40:1570-1573.
[6]	Hashimoto DA, Rosman G, Rus D, et al. Artificial intelligence in surgery:promises and perils[J]. Ann Surg, 2018, 268:70-76.
[7]	Schulte-Sasse R, Budach S, Hnisz D, et al. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms[J]. Nat Mach Intell, 2021, 3:513-526.
[8]	Li J, Chen H, Wang Y, et al. Next-generation analytics for omics data[J]. Cancer Cell, 2021, 39:3-6.
[9]	Poore GD, Kopylova E, Zhu Q, et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach[J]. Nature, 2020, 579:567-574.
[10]	Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer[J]. Nature, 2019, 570:385-389.
[11]	Yu KH, Hu V, Wang F, et al. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks[J]. BMC Med, 2020, 18:1-14.
[12]	Saillard C, Schmauch B, Laifa O, et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides[J]. Hepatology, 2020, 72:2000-2013.
[13]	王玉峰, 蔡文杰. IBM沃森成败录[J]. 中国工业和信息化, 2020:78-83. doi:10.19609/j.cnki.cn10-1299/f.2022.zl.017.
[14]	Yang X, Wang Y, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery[J]. Chem Rev, 2019, 119:10520-10594.
[15]	Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596:583-589.
[16]	Stokes JM, Yang K, Swanson K, et al. A Deep learning approach to antibiotic discovery[J]. Cell, 2020, 180:688-702.
[17]	Julkunen H, Cichonska A, Gautam P, et al. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects[J]. Nat Commun, 2020, 11. doi:10.1038/S41467-020-19950-Z.
[18]	Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells[J]. Cancer Cell, 2020, 38:672-684.
[19]	Bibault JE, Bassenne M, Ren H, et al. Deep learning prediction of cancer prevalence from satellite imageryp[J]. Cancers (Basel), 2020, 12:3844. doi:10.3390/cancers12123844.
[20]	Paparrizos J, White RW, Horvitz E. Screening for pancreatic adenocarcinoma using signals from web search logs:feasibility study and results[J]. J Oncol Pract, 2016,12:737-744.
[21]	Beauchamp UL, Pappot H, Holländer-Mieritz C. The use of wearables in clinical trials during cancer treatment:systematic review[J]. JMIR Mhealth Uhealth, 2020, 8:e22006. doi:10.2196/22006.
[22]	Wilkinson MD, Dumontier M, Aalbersberg LJ, et al. The FAIR guiding principles for scientific data management and stewardship[J]. Sci Data,2016, 3:1-9.
[23]	Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics:current applications and future directions[J]. J Endod, 2021, 47:1352-1357.