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Analysis of relationship between tumor markers and detection of tumors by chemiluminescence immunoassay and artificial neural networks

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A Correction to this article was published on 07 August 2023

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Abstract

The early detection and treatment of tumors play a crucial role in reducing their high risk and mortality rates. Tumor markers with high sensitivity and specificity have emerged as valuable indicators for tumor diagnosis and prognosis prediction. Among the detection methods, chemiluminescence immunoassay (CLIA) has gained significant attention due to its advantages of high sensitivity, wide detection range, simplicity, repeatability, specificity, automation, and absence of radioactive reagents. This paper proposes a novel approach that combines CLIA for tumor marker detection with artificial neural network (ANN) analysis for cancer classification and screening. The research includes an overview of CLIA’s status, introduction of the SAE neural network model, selection of evaluation indices, and construction of the optimal SAE model. In this paper, CLIA is employed to detect the relationship between tumor markers and tumors. An overview of CLIA's research status is presented, providing a theoretical foundation for the proposed analysis method. The technical principles of ANN are introduced, and the SAE neural network model is proposed. Model evaluation indices are selected, and through experiments, the optimal SAE model is constructed by determining the SAE parameters. Sample data are inputted, and the model's accuracy, recall, and F1 score are obtained. A comparison with other models reveals that the SAE model proposed in this paper exhibits the best detection performance. The results demonstrate that the proposed SAE model outperforms other models, exhibiting superior detection performance.

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References

  • Aidossov N, Zarikas V, Zhao Y et al (2023) An integrated intelligent system for breast cancer detection at early stages using IR images and machine learning methods with explainability. SN Comput Sci. https://doi.org/10.1007/s42979-022-01536-9

    Article  Google Scholar 

  • Barak V, Goike H, Panaretakis KW et al (2004) Clinical utility of cytokeratins as tumor markers. Clin Biochem 37(7):529–540

    Article  Google Scholar 

  • Busari SA, Huq KMS, Mumtaz S et al (2019) Generalized hybrid beamforming for vehicular connectivity using THz massive MIMO. IEEE Trans Veh Technol 68(9):8372–8383

    Article  Google Scholar 

  • Chen W, Jie WU, Chen Z et al (2012) Chemiluminescent immunoassay and its applications. Chin J Anal Chem 40(1):3–10

    Article  Google Scholar 

  • Chen Y, Liu H, Wang X (2019) Integration of chemiluminescence immunoassay and artificial neural networks for improved cancer screening. Sensors 19(4):856

    Google Scholar 

  • Du J, Jiang C, Han Z et al (2019) Contract mechanism and performance analysis for data transaction in mobile social networks. IEEE Trans Netw Sci Eng 6(2):103–115

    Article  Google Scholar 

  • Duffy MJ (2001) Clinical uses of tumor markers: a critical review. Crit Rev Clin Lab Sci 38(3):225–262

    Article  Google Scholar 

  • Duffy MJ (2006) Serum tumor markers in breast cancer: are they of clinical value? Clin Chem 52(3):345–351

    Article  Google Scholar 

  • Hayes DF, Bast RC, Desch CE et al (1996) Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers. JNCI J Natl Cancer Inst 88(20):1456–1466

    Article  Google Scholar 

  • Kaur M (2023) AI- and IoT-based energy saving mechanism by minimizing hop delay in multi-hop and advanced optical system based optical channels. Opt Quant Electron 55:635. https://doi.org/10.1007/s11082-023-04882-x

    Article  Google Scholar 

  • Kaur M, Sakhare SR, Wanjale K et al (2022) Early stroke prediction methods for prevention of strokes. Behav Neurol. https://doi.org/10.1155/2022/7725597

    Article  Google Scholar 

  • Kaur M, Khedkar G, Sakhare S et al (2023) A research study on the cervical cerclage to deal with cervical insufficiency using machine learning. Soft Comput. https://doi.org/10.1007/s00500-023-08622-x

    Article  Google Scholar 

  • Krasowski MD, Pizon AF, Siam MG et al (2009) Using molecular similarity to highlight the challenges of routine immunoassay-based drug of abuse/toxicology screening in emergency medicine. BMC Emer Med 9(1):1–18

    Google Scholar 

  • Lone SN, Nisar S, Masoodi T et al (2022) Liquid biopsy: a step closer to transform diagnosis, prognosis and future of cancer treatments. Mol Cancer 21:79. https://doi.org/10.1186/s12943-022-01543-7

    Article  Google Scholar 

  • Ma L, Sun Y, Kang X et al (2014) Development of nanobody-based flow injection chemiluminescence immunoassay for sensitive detection of human prealbumin. Biosens Bioelectron 61:165–171

    Article  Google Scholar 

  • Mokoatle M, Marivate V, Mapiye D et al (2023) A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. BMC Bioinform. https://doi.org/10.1186/s12859-023-05235-x

    Article  Google Scholar 

  • Molina R, Barak V, van Dalen A et al (2005) Tumor markers in breast cancer–European Group on Tumor Markers recommendations. Tumor Biol 26(6):281–293

    Article  Google Scholar 

  • Nagpal M, Singh S, Singh P et al (2016) Tumor markers: a diagnostic tool. Natl J Maxillofac Surg 7(1):17

    Article  Google Scholar 

  • Padoan A, Cosma C, Sciacovelli L et al (2020) Analytical performances of a chemiluminescence immunoassay for SARS-CoV-2 IgM/IgG and antibody kinetics. Clin Chem Lab Med 58(7):1081–1088

    Article  Google Scholar 

  • Perkins GL, Slater ED, Sanders GK et al (2003) Serum tumor markers. Am Fam Physician 68(6):1075–1082

    Google Scholar 

  • Qin X, Lin JM (2015) Advances and applications of chemiluminescence immunoassay in clinical diagnosis and foods safety. Chin J Anal Chem 43(6):929–938

    Article  Google Scholar 

  • Qiu Y, Li P, Dong S et al (2018) Phage-mediated competitive chemiluminescent immunoassay for detecting Cry1Ab toxin by using an anti-idiotypic camel nanobody. J Agric Food Chem 66(4):950–956

    Article  Google Scholar 

  • Qiu Y, Li P, Liu B et al (2019) Phage-displayed nanobody based double antibody sandwich chemiluminescent immunoassay for the detection of Cry2A toxin in cereals. Food Agric Immunol 30(1):924–936

    Article  Google Scholar 

  • Wang T, Zhang F, Gu H, Hu H et al (2023) A research study on new energy brand users based on principal component analysis (PCA) and fusion target planning model for sustainable environment of smart cities. Sustain Energy Technol Assess 57:103262

    Google Scholar 

  • Wei SJ, Wang LP, Wang JY et al (2021) Diagnostic value of imaging combined with tumor markers in early detection of lung cancer. Front Surg 26(8):694210

    Article  Google Scholar 

  • Xiao Q, Xu C (2020) Research progress on chemiluminescence immunoassay combined with novel technologies. TrAC Trends Anal Chem 124:115780

    Article  Google Scholar 

  • Xu J, Wu J, Zong C et al (2013) Manganese porphyrin-dsDNA complex: a mimicking enzyme for highly efficient bioanalysis. Anal Chem 85(6):3374–3379

    Article  Google Scholar 

  • Yang H, Bever CS, Zhang H et al (2019) Comparison of soybean peroxidase with horseradish peroxidase and alkaline phosphatase used in immunoassays. Anal Biochem 581:113336

    Article  Google Scholar 

  • Zhang L, Wang Q, Li J (2018) Application of chemiluminescence immunoassay and artificial neural networks in cancer diagnosis. J Clin Lab Anal 32(8):e22599

    Google Scholar 

  • Zong C, Wu J, Wang C et al (2012) Anal Chem 84(5):2410–2415

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Authors and Affiliations

  1. Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Suzhou, China

    Qinglan Zhu & Guofei Chen

  2. Wuzhong People’s Hospital, Suzhou, China

    Zhenjun Mao

Authors
  1. Qinglan Zhu
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  2. Zhenjun Mao
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  3. Guofei Chen
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The authors have contributed equally for data collection, investigation, and writing the paper.

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Correspondence to Guofei Chen.

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Zhu, Q., Mao, Z. & Chen, G. Analysis of relationship between tumor markers and detection of tumors by chemiluminescence immunoassay and artificial neural networks. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08855-w

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