Artificial Intelligence for the Identification of Biomarkers in Cancer Prevention and Diagnosis: Advances and Perspectives
DOI:
https://doi.org/10.32635/2176-9745.RBC.2024v70n2.4692Keywords:
Neoplasias/epidemiologia, Inteligência Artificial, Detecção Precoce de Câncer/métodos, Diagnóstico Precoce, Biomarcadores/análiseAbstract
Introduction: The systematic analysis of cancer markers and the impact of artificial intelligence (AI) on early detection and therapeutic approach are crucial in today’s medical field. Cancer represents a significant global burden of morbidity and mortality, making early identification of markers a priority for effective disease management. This study aims to explore recent advancements in the identification and characterization of cancer indicators, including genetic, molecular, protein, and imaging biomarkers. Objective: To analyze the latest advances in identifying and characterizing cancer indicators, covering a variety of biomarker types. Additionally, to investigate the role of AI in improving and applying methods for cancer detection, diagnosis, prognosis, and treatment, highlighting its significant contributions to enhancing the accuracy and efficiency of these approaches. Method: A systematic literature review was conducted, selecting relevant studies addressing the identification of cancer biomarkers and the use of AI in this context based on specific inclusion and exclusion criteria. Results: The results of this systematic analysis highlight recent advances in identifying and characterizing cancer indicators, as well as the impact of AI on enhancing detection, diagnosis, prognosis, and treatment approaches. Conclusion: This study offers valuable insights into the role of cancer indicators and AI in disease prevention and management, supporting evidence-based clinical practices and promoting the development of more efficient and personalized healthcare approaches.
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Brito-Rocha T, Constâncio V, Henrique R, et al. Shifting the cancer screening paradigm: the rising potential of blood-based multi-cancer early detection tests. Cells. 2023;12(6):935. doi: https://doi.org/10.3390/cells12060935 DOI: https://doi.org/10.3390/cells12060935
Instituto Nacional do Câncer José Alencar Gomes da Silva. Detecção Precoce do Câncer [Internet]. Rio de Janeiro: INCA; 2021. [acesso 2024 fev 9]. Disponível em: https://www.inca.gov.br/sites/ufu.sti.inca.local/files/media/document/deteccao-precoce-do-cancer.pdf
Ibrahim A, Gamble P, Jaroensri R, et al. Artificial intelligence in digital breast pathology: techniques and applications. The Breast. 2020;49:267-73. doi: https://doi.org/10.1016/j.breast.2019.12.007 DOI: https://doi.org/10.1016/j.breast.2019.12.007
Hunter B, Hindocha S, Lee RW. The Role of artificial intelligence in early cancer diagnosis. Cancers (Basel). 2022;14(6):1524. doi: https://doi.org/10.3390/cancers14061524 DOI: https://doi.org/10.3390/cancers14061524
Fitzgerald J, Higgins D, Mazo Vargas C, et al. Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer. J Clin Pathol. 2021;74(7):429-34. doi: https://doi.org/10.1136/jclinpath-2020-207351 DOI: https://doi.org/10.1136/jclinpath-2020-207351
Page MJ, Moher D, Bossuyt PM, et al. Prisma 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. doi: https://doi.org/10.1136/bmj.n160 DOI: https://doi.org/10.1136/bmj.n160
University of York. Centre for Reviews and Dissemination. New York: University of York; 2019. PROSPERO - International prospective register of systematic reviews. 2023. [acesso 2023 ago 31]. Disponível em: https://www.crd.york.ac.uk/PROSPERO/
Barreto MS, Carreira L, Marcon SS. Envelhecimento populacional e doenças crônicas: reflexões sobre os desafios para o Sistema de Saúde Pública. Kairós-Gerontologia. 2015;18(1)325-39. doi: https://doi.org/10.23925/2176-901X.2015v18i1p325-339
Davis AA, Gerratana L, Mina M. Cancer evolution: from biological insights to therapeutic opportunities. Front Genet. 2022;13:984032. doi: https://doi.org/10.3389/fgene.2022.984032 DOI: https://doi.org/10.3389/fgene.2022.984032
Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321-32. doi: https://doi.org/10.1038/nrg3920 DOI: https://doi.org/10.1038/nrg3920
Ahmed Z, Mohamed K, Zeeshan S, et al. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020:baaa010. doi: https://doi.org/10.1093/database/baaa010 DOI: https://doi.org/10.1093/database/baaa010
Esteva A, Kuprel B, Novoa R, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. doi: https://doi.org/10.1038/nature21056 DOI: https://doi.org/10.1038/nature21056
Zhu Y, Wang M, Yin X, et al. Deep learning in diverse intelligent sensor based systems. Sensors (Basel). 2022;23(1):62. doi: https://doi.org/10.3390/s23010062 DOI: https://doi.org/10.3390/s23010062
Wang T, Antonacci-Fulton L, Howe K. et al. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604. 2022;437-46. DOI: https://doi.org/10.1038/s41586-022-04601-8
Alves-Ferreira M, Guimarães ACR, Cuervo P, et al. Abordagens genômicas, transcriptômicas e proteômicas aplicadas ao estudo de leishmania spp. In: Conceição- Silva F, Alves CR, compiladores. Leishmanioses do continente americano. Rio de Janeiro: Editora Fiocruz; 2014. p. 491-511.
Abram KJ, McCloskey D. A Comprehensive evaluation of metabolomics data preprocessing methods for deep learning. Metabolites. 2022;12(3):202. doi: https://doi.org/10.3390/metabo12030202 DOI: https://doi.org/10.3390/metabo12030202
Chaudhary K, Poirion OB, Lu L, et al. Deep learningbased multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 2018;24(6):1248-59. doi: https://doi.org/10.1158/1078-0432.ccr-17-0853 DOI: https://doi.org/10.1158/1078-0432.CCR-17-0853
Liu G, Dong C, Liu L. Integrated multiple “-omics” data reveal subtypes of hepatocellular carcinoma. PLoS One. 2016;11(11):e0165457. doi: https://doi.org/10.1371/journal.pone.0165457 DOI: https://doi.org/10.1371/journal.pone.0165457
Cifci D, Foersch S, Kather JN. Artificial intelligence to identify genetic alterations in conventional histopathology. J Pathol. 2022;257(4):430-44. doi: https://doi.org/10.1002/path.5898 DOI: https://doi.org/10.1002/path.5898
O’Donnell ST, Ross RP, Stanton C. The Progress of multi-omics technologies: determining function in lactic acid bacteria using a systems level approach. Front Microbiol. 2020;10:3084. doi: https://doi.org/10.3389/fmicb.2019.03084 DOI: https://doi.org/10.3389/fmicb.2019.03084
Gao Q, Yang L, Lu M, et al. The artificial intelligence and machine learning in lung cancer immunotherapy. J Hematol Oncol. 2023;16(1):55. doi: https://doi.org/10.1186/s13045-023-01456-y DOI: https://doi.org/10.1186/s13045-023-01456-y
Alakwaa FM, Chaudhary K, Garmire LX. Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J Proteome Res. 2018;17(1):337-47. doi: https://doi.org/10.1021/acs.jproteome.7b00595 DOI: https://doi.org/10.1021/acs.jproteome.7b00595
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48. doi: https: //doi.org/10.1146/annurevbioeng-071516-044442 DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442
Abdelrahman L, Ghamdi M, Collado-Mesa F, et al. Convolutional neural networks for breast cancer detection in mammography: a survey. Computers in biology and medicine. 2021; v. 131, p. 104248. DOI: https://doi.org/10.1016/j.compbiomed.2021.104248
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. Errata in: Nature. 2020;586(7829):E19. doi: https://doi.org/10.1038/s41586-019-1799-6 DOI: https://doi.org/10.1038/s41586-020-2679-9
Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-61. https://doi.org/10.1038/s41591-019-0447-x DOI: https://doi.org/10.1038/s41591-019-0447-x
Li H, Lee CH, Chia D, et al. Machine learning in prostate mri for prostate cancer: current status and future opportunities. Diagnostics (Basel). 2022;12(2):289. doi: https://doi.org/10.3390/diagnostics12020289 DOI: https://doi.org/10.3390/diagnostics12020289
Lahiri A, Maji A, Potdar PD, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer. 2023;22(1):40. doi: https://doi.org/10.1186/s12943-023-01740-y DOI: https://doi.org/10.1186/s12943-023-01740-y
Chen YJ, Abila B, Mostafa Kamel Y. CAR-T: What is next? Cancers (Basel). 2023;15(3):663. doi: https://doi.org/10.3390/cancers15030663 DOI: https://doi.org/10.3390/cancers15030663
Sterner RC, Sterner RM. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 2021;11(4):69. doi: https://doi.org/10.1038/s41408-021-00459-7 DOI: https://doi.org/10.1038/s41408-021-00459-7
Zhang X, Zhu L, Zhang H, et al. CAR-T cell therapy in hematological malignancies: current opportunities and challenges. Front Immunol. 2022;13:927153. doi: https://doi.org/10.3389/fimmu.2022.927153 DOI: https://doi.org/10.3389/fimmu.2022.927153
Instituto Nacional de Câncer. Estimativa 2023: Incidência de câncer no Brasil [Internet]. Rio de Janeiro: INCA; 2022. [acesso 2024 jan 1]. Disponível em: https://www.inca.gov.br/sites/ufu.sti.inca.local/files//media/document//estimativa-2023.pdf
Santos MO, Lima FCS, Martins LFL, et al. Estimativa de incidência de câncer no Brasil, 2023-2025. Rev Bras Cancerol. 2023;69(1):e-213700. doi: https://doi.org/10.32635/2176-9745.RBC.2023v69n1.3700 DOI: https://doi.org/10.32635/2176-9745.RBC.2023v69n1.3700
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