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REVIEW

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

, &
Pages 1779-1791 | Received 15 Mar 2023, Accepted 12 Jun 2023, Published online: 26 Jun 2023

References

  • Harbeck N, Gnant M. Breast cancer. Lancet. 2017;389(10074):1134–1150. doi:10.1016/S0140-6736(16)31891-8
  • Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. doi:10.3322/caac.21660
  • Afolabi LO, Afolabi MO, Sani MM, et al. Exploiting the CRISPR‐Cas9 gene‐editing system for human cancers and immunotherapy. Clinical Translat Immunol. 2021;10(6):e1286. doi:10.1002/cti2.1286
  • World Health Organization. © International Agency for Research on Cancer, 2020. Cancer Today. Available from:https://gco.iarc.fr/today/online-analysis-pie?v=2020&mode=cancer&mode_population=continents&population=900&populations=900&key=total&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=7&group_cancer=1&include_nmsc=1&include_nmsc_other=1&half_pie=0&donut=0. Accessed June 20, 2023.
  • Ahmad M, Khan Z, Rahman ZU, Khattak SI, Khan ZU. Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective. Econ Innov New Technol. 2021;30(1):89–109. doi:10.1080/10438599.2019.1684643
  • Gaur K, Jagtap MM. Role of artificial intelligence and machine learning in prediction, diagnosis, and prognosis of cancer. Cureus. 2022;14(11). doi:10.7759/cureus.31008
  • Dananjayan S, Raj GM. Artificial Intelligence during a pandemic: the COVID −19 example. Int J Health Plann Manage. 2020;35:1260–1262. doi:10.1002/hpm.2987
  • Iqbal MJ, Javed Z, Sadia H, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int. 2021;21(1):1–11. doi:10.1186/s12935-021-01981-1
  • Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 2020;471:61–71. doi:10.1016/j.canlet.2019.12.007
  • Hollon TC, Pandian B, Adapa AR, et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26(1):52–58. doi:10.1038/s41591-019-0715-9
  • Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1):389–422. doi:10.1023/A:1012487302797
  • Mori Y, Kudo SE. Detecting colorectal polyps via machine learning. Nat Biomed Eng. 2018;2(10):713–714. doi:10.1038/s41551-018-0308-9
  • Wang K-W, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: recent advances and prospects. World J Gastroenterol. 2020;26(34):5090. doi:10.3748/wjg.v26.i34.5090
  • Jianzhu B, Shuang L, Pengfei M, Yi Z, Yanshu Z. Research on early warning mechanism and model of liver cancer rehabilitation based on CS-SVM. J Healthc Eng. 2021;2021. doi:10.1155/2021/6658776
  • Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391–403. doi:10.1038/s41585-019-0193-3
  • Hu F, Shi X, Wang H, et al. Is health contagious?—Based on empirical evidence from China family panel studies’ data. Front Public Health. 2021;9:691746. doi:10.3389/fpubh.2021.691746
  • Musa IH, Afolabi LO, Zamit I, et al. Artificial intelligence and machine learning in cancer research: a systematic and thematic analysis of the top 100 cited articles indexed in Scopus database. Cancer Control. 2022;29:10732748221095946. doi:10.1177/10732748221095946
  • Kubat M. An Introduction to Machine Learning. Springer; 2017.
  • Alpaydin E. Introduction to Machine Learning. MIT press; 2020.
  • Rebala G, Ravi A, Churiwala S. An Introduction to Machine Learning. Springer; 2019.
  • Jovel J, Greiner R. An introduction to machine learning approaches for biomedical research. Front Med. 2021;2021:8.
  • Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. doi:10.1186/s12874-019-0681-4
  • Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–260. doi:10.1126/science.aaa8415
  • Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056
  • Anderson JP, Parikh JR, Shenfeld DK, et al. Willke RJ: reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records. J Diabetes Sci Technol. 2016;10(1):6–18. doi:10.1177/1932296815620200
  • Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Practical guidance on artificial intelligence for health-care data. Lancet Digital Health. 2019;1(4):e157–e159. doi:10.1016/S2589-7500(19)30084-6
  • Ertel W. Introduction to Artificial Intelligence. Springer; 2018.
  • Kalis B, Collier M, Fu R. 10 promising AI applications in health care. Harv Bus Rev. 2018;2018:1.
  • Wang H, Zu Q, Chen J, Yang Z, Ahmed MA. Application of artificial intelligence in acute coronary syndrome: a brief literature review. Adv Ther. 2021;38(10):5078–5086. doi:10.1007/s12325-021-01908-2
  • Cao J-S, Z-Y L, Chen M-Y, et al. Artificial intelligence in gastroenterology and hepatology: status and challenges. World J Gastroenterol. 2021;27(16):1664. doi:10.3748/wjg.v27.i16.1664
  • Tran NK, Albahra S, May L, et al. Evolving applications of artificial intelligence and machine learning in infectious diseases testing. Clin Chem. 2022;68(1):125–133. doi:10.1093/clinchem/hvab239
  • Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337–339. doi:10.1016/j.dsx.2020.04.012
  • Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021;11(4):900–915. doi:10.1158/2159-8290.CD-21-0090
  • Yu C, Helwig EJ. The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artif Intell Rev. 2022;55(1):323–343. doi:10.1007/s10462-021-10034-y
  • Kumar Y, Gupta S, Singla R. Hu Y-C: a systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng. 2021;2021:1–28.
  • McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94. doi:10.1038/s41586-019-1799-6
  • Majumder A, Sen D. Artificial intelligence in cancer diagnostics and therapy: current perspectives. Indian J Cancer. 2021;58(4):481. doi:10.4103/ijc.IJC_399_20
  • Pantanowitz L, Quiroga-Garza GM, Bien L, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digital Health. 2020;2(8):e407–e416. doi:10.1016/S2589-7500(20)30159-X
  • Feng H, Yang B, Wang J, et al. Identifying Malignant Breast Ultrasound Images Using ViT-Patch. Appl Sci. 2023;13(6):3489. doi:10.3390/app13063489
  • Ray A, Chen M, Gelogo Y. Performance Comparison of Different Machine Learning Algorithms for Risk Prediction and Diagnosis of Breast Cancer. In: Smart Technologies in Data Science and Communication. Springer; 2020:71–76.
  • Rana M, Chandorkar P, Dsouza A, Kazi N. Breast cancer diagnosis and recurrence prediction using machine learning techniques. Int J Eng Res Technol. 2015;4(4):372–376. doi:10.15623/ijret.2015.0404066
  • Kharya S, Dubey D, Soni S. Predictive machine learning techniques for breast cancer detection. Int J Comput Sci Inf Technol. 2013;4(6):1023–1028.
  • Liu H, Liu M, Li D, Zheng W, Yin L, Wang R. Recent advances in pulse-coupled neural networks with applications in image processing. Electronics. 2022;11(20):3264. doi:10.3390/electronics11203264
  • Agrawal S, Agrawal J. Neural network techniques for cancer prediction: a survey. Procedia Comput Sci. 2015;60:769–774. doi:10.1016/j.procs.2015.08.234
  • Agarap AFM. On breast cancer detection: an application of machine learning algorithms on the Wisconsin diagnostic dataset. In: Proceedings of the 2nd international conference on machine learning and soft computing; 2018:5–9.
  • Enshaei A, Robson C, Edmondson R. Artificial intelligence systems as prognostic and predictive tools in ovarian cancer. Ann Surg Oncol. 2015;22(12):3970–3975. doi:10.1245/s10434-015-4475-6
  • Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016;6(1):1–10. doi:10.1038/srep26094
  • Nartowt BJ, Hart GR, Muhammad W, Liang Y, Stark GF, Deng J. Robust machine learning for colorectal cancer risk prediction and stratification. Front Big Data. 2020;3:6. doi:10.3389/fdata.2020.00006
  • Nartowt BJ, Hart GR, Roffman DA, et al. Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data. PLoS One. 2019;14(8):e0221421. doi:10.1371/journal.pone.0221421
  • Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS One. 2018;13(10):e0205264. doi:10.1371/journal.pone.0205264
  • Stark GF, Hart GR, Nartowt BJ, Deng J. Predicting breast cancer risk using personal health data and machine learning models. PLoS One. 2019;14(12):e0226765. doi:10.1371/journal.pone.0226765
  • Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep. 2018;8(1):1–7. doi:10.1038/s41598-018-19907-9
  • Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell. 2019;2:2. doi:10.3389/frai.2019.00002
  • Zhao H, Ming T, Tang S, et al. Wnt signaling in colorectal cancer: pathogenic role and therapeutic target. Mol Cancer. 2022;21(1):144. doi:10.1186/s12943-022-01616-7
  • Tian Y, Xiao H, Yang Y, et al. Crosstalk between 5-methylcytosine and N6-methyladenosine machinery defines disease progression, therapeutic response and pharmacogenomic landscape in hepatocellular carcinoma. Mol Cancer. 2023;22(1):1–25. doi:10.1186/s12943-022-01706-6
  • Wrzeszczynski KO, Frank MO, Koyama T, et al. Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma. Neurol Genet. 2017;3(4):e164. doi:10.1212/NXG.0000000000000164
  • Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020;18:2300–2311. doi:10.1016/j.csbj.2020.08.019
  • Capper D, Stichel D, Sahm F, et al. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta Neuropathol. 2018;136(2):181–210. doi:10.1007/s00401-018-1879-y
  • Lv Z, Yu Z, Xie S, Alamri A. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare. ACM Trans Multimedia Comput Commun Appl. 2022;18(1):1–20.
  • Gupta S, Gupta MK, Shabaz M, Sharma A. Deep learning techniques for cancer classification using microarray gene expression data. Front Physiol. 2022;2002:1.
  • Gupta S, Gupta M. Deep learning for brain tumor segmentation using magnetic resonance images. In: 2021 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB); IEEE; 2021:1–6.
  • Dwivedi AK. Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput Appl. 2018;29(12):1545–1554. doi:10.1007/s00521-016-2701-1
  • Gupta S, Gupta MK. Computational prediction of cervical cancer diagnosis using ensemble-based classification algorithm. Comput J. 2022;65(6):1527–1539. doi:10.1093/comjnl/bxaa198
  • Tumuluru P, Ravi B. GOA-based DBN: grasshopper optimization algorithm-based deep belief neural networks for cancer classification. Int J Appl Eng Res. 2017;12(24):14218–14231.
  • Danaee P, Ghaeini R, Hendrix DA: A deep learning approach for cancer detection and relevant gene identification. In: Pacific symposium on biocomputing 2017; World Scientific; 2017:219–229.
  • Bębas E, Borowska M, Derlatka M, et al. Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis. Biomed Signal Process Control. 2021;66:102446. doi:10.1016/j.bspc.2021.102446
  • Yang S, Li Q, Li W, Li X, Liu -A-A. Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans Circuits Syst Video Technol. 2022;32(11):8037–8050. doi:10.1109/TCSVT.2022.3182426
  • Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol. 2020;196(10):879–887. doi:10.1007/s00066-020-01625-9
  • Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol. 2020;196(10):888–899. doi:10.1007/s00066-020-01615-x
  • Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020;196(10):856–867. doi:10.1007/s00066-020-01626-8
  • Bae S, An C, Ahn SS, et al. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep. 2020;10(1):1–10. doi:10.1038/s41598-020-68980-6
  • Bibault J-E, Chang DT, Xing L. Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884–889. doi:10.1136/gutjnl-2020-321799
  • Senders JT, Staples P, Mehrtash A, et al. An online calculator for the prediction of survival in glioblastoma patients using classical statistics and machine learning. Neurosurgery. 2020;86(2):E184–E192. doi:10.1093/neuros/nyz403
  • Kim DW, Lee S, Kwon S, Nam W, Cha I-H, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep. 2019;9(1):1–10. doi:10.1038/s41598-018-37186-2
  • Matsuo K, Machida H, Shoupe D, et al. Ovarian conservation and overall survival in young women with early-stage low-grade endometrial cancer. Obstet Gynecol. 2016;128(4):761. doi:10.1097/AOG.0000000000001647
  • Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke Vasc Neurol. 2019;4(4):206–213. doi:10.1136/svn-2019-000290
  • Liu -A-A, Zhai Y, Xu N, Nie W, Li W, Zhang Y. Region-aware image captioning via interaction learning. IEEE Trans Circuits Syst Video Technol. 2021;32(6):3685–3696. doi:10.1109/TCSVT.2021.3107035
  • Basu K, Sinha R, Ong A, Basu T. Artificial intelligence: how is it changing medical sciences and its future? Indian J Dermatol. 2020;65(5):365–370. doi:10.4103/ijd.IJD_421_20
  • Sivashanker K, Duong T, Resnick A, Eappen S. Health care equity: from fragmentation to transformation. NEJM Catal Innov Care Deliv. 2020;15.
  • Zhang P, Schmidt DC, White J, Lenz G. Chapter One - Blockchain Technology Use Cases in Healthcare. In: Raj P, Deka GC, editors. Advances in Computers. Vol. 111. Elsevier; 2018:1–41.
  • Gay V, Leijdekkers P. Bringing health and fitness data together for connected health care: mobile apps as enablers of interoperability. J Med Internet Res. 2015;17(11):e260. doi:10.2196/jmir.5094
  • Ranchal R, Bastide P, Wang X, et al. Disrupting healthcare silos: addressing data volume, velocity and variety with a cloud-native healthcare data ingestion service. IEEE J Biomed Health Inform. 2020;24(11):3182–3188. doi:10.1109/JBHI.2020.3001518
  • Alvarez-Romero C, Martínez-García A, Sinaci AA, et al. FAIR4Health: findable, accessible, interoperable and reusable data to foster health research. Open Res Eur. 2022;2:34. doi:10.12688/openreseurope.14349.1
  • Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3(1):160018. doi:10.1038/sdata.2016.18
  • Parra-Calderón CL, Sanz F, McIntosh LD. The challenge of the effective implementation of FAIR principles in biomedical research. Methods Inf Med. 2020;59(04/05):117–118. doi:10.1055/s-0040-1721726
  • Folorunso S, Ogundepo E, Basajja M, et al. FAIR machine learning model pipeline implementation of COVID-19 data. Data Intelli. 2022;4(4):971–990. doi:10.1162/dint_a_00182
  • Zamini M, Reza H, Rabiei M. A review of knowledge graph completion. Information. 2022;13(8):396. doi:10.3390/info13080396
  • Stokman FN, de Vries PH. Structuring Knowledge in a Graph. Rijksuniversiteit Groningen; 1986.
  • Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence; 2014.
  • Nie W, Bao Y, Zhao Y, Liu A. Long dialogue emotion detection based on commonsense knowledge graph guidance. IEEE Trans Multimedia. 2023;1–15. doi:10.1109/TMM.2023.3267295
  • Ji S, Pan S, Cambria E, Marttinen P, Philip SY. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Tran Neural Net Learn Sys. 2021;33(2):494–514. doi:10.1109/TNNLS.2021.3070843
  • Mohamed SK, Nounu A, Nováček V. Biological applications of knowledge graph embedding models. Brief Bioinform. 2021;22(2):1679–1693. doi:10.1093/bib/bbaa012
  • Zeng X, Tu X, Liu Y, Fu X, Su Y. Toward better drug discovery with knowledge graph. Curr Opin Struct Biol. 2022;72:114–126. doi:10.1016/j.sbi.2021.09.003
  • Hasan SMS, Rivera D, Wu XC, Durbin EB, Christian JB, Tourassi G. Knowledge graph-enabled cancer data analytics. IEEE J Biomed Health Inform. 2020;24(7):1952–1967. doi:10.1109/JBHI.2020.2990797
  • Gogleva A, Polychronopoulos D, Pfeifer M, et al. Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer. Nat Commun. 2022;13(1):1667. doi:10.1038/s41467-022-29292-7
  • Alawad M, Gao S, Shekar MC, et al. Integration of domain knowledge using medical knowledge graph deep learning for cancer phenotyping. arXiv preprint arXiv. 2021;2021:210101337.
  • Rowe M. An introduction to machine learning for clinicians. Acad Med. 2019;94(10):1433–1436. doi:10.1097/ACM.0000000000002792
  • Gao Q, Zhu H, Dong L, et al. Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma. Cell. 2019;179(2):561–577. e522. doi:10.1016/j.cell.2019.08.052
  • Lucas GM, Gratch J, King A, Morency L-P. It’s only a computer: virtual humans increase willingness to disclose. Comput Human Behav. 2014;37:94–100. doi:10.1016/j.chb.2014.04.043
  • Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenge. J Med Internet Res. 2019;21(7):e13659. doi:10.2196/13659
  • Li W, Zhang Y, Chen F. ChatGPT in colorectal surgery: a promising tool or a passing fad? Ann Biomed Eng. 2023. doi:10.1007/s10439-023-03232-y
  • Cifarelli CP, Sheehan JP. Large language model artificial intelligence: the current state and future of ChatGPT in neuro-oncology publishing. J Neurooncol. 2023. doi:10.1007/s11060-023-04336-0