Research Article | | Peer-Reviewed

Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer

Received: 17 December 2023     Accepted: 28 December 2023     Published: 8 January 2024
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Abstract

Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer

Published in Clinical Medicine Research (Volume 13, Issue 1)
DOI 10.11648/j.cmr.20241301.12
Page(s) 6-12
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Breast Cancer, Machine Learning, FER, CEA, CA153, CY211, Clinical Prediction Model

References
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[2] Di Sibio A, Abriata G, Forman D, Sierra MS. Female breast cancer in Central and South America. Cancer Epidemiol. 2016; 44 Suppl 1: S110-S120. doi: 10.1016/j.canep.2016.08.010.
[3] Duffy MJ, et al. Tumor markers in breast cancer: Guidelines for use and interpretation in the clinical setting. European Journal of Cancer. 2005; 41(9): 1343-1357. doi: 10.1016/j.ejca.2005.02.025.
[4] K. Pantel, "Abstract PL01-01: Liquid biopsy: Novel technologies and clinical applications", Clinical Research (Excluding Clinical Trials), 2018. DOI: 10.1158/1538-7445. AM2018-PL01-01.
[5] M. Zubair, S. Wang, N. Ali, "Advanced Approaches to Breast Cancer Classification and Diagnosis", Frontiers in Pharmacology, 2021. DOI: 10.3389/fphar.2020.632079.
[6] Niell BL, Freer PE, Weinfurtner RJ, Arleo EK, Drukteinis JS. Screening for Breast Cancer. Radiol Clin North Am. 2017; 55(6): 1145-1162. doi: 10.1016/j.rcl.2017.06.004.
[7] Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016; 316(22): 2402-2410. doi: 10.1001/jama.2016.17216.
[8] Lakhani P, Prater AB, Hutson RK, et al. Machine Learning in Radiology: Applications Beyond Image Interpretation. J Am Coll Radiol. 2018; 15(2): 350-359. doi: 10.1016/j.jacr.2017.09.044.
[9] Savarese TM, Strohsnitter WC, Low HP, et al. Correlation of umbilical cord blood hormones and growth factors with stem cell potential: implications for the prenatal origin of breast cancer hypothesis. Breast Cancer Res. 2007; 9(3): R29. doi: 10.1186/bcr1674.
[10] Giovanella L, Ceriani L, Giardina G, et al. Serum cytokeratin fragment 21.1 (CYFRA 21.1) as tumour marker for breast cancer: comparison with carbohydrate antigen 15.3 (CA 15.3) and carcinoembryonic antigen (CEA). Clin Chem Lab Med. 2002; 40(3): 298-303. doi: 10.1515/CCLM.2002.047.
[11] Tarighati E, Keivan H, Mahani H. A review of prognostic and predictive biomarkers in breast cancer. Clin Exp Med. 2023; 23(1): 1-16. doi: 10.1007/s10238-021-00781-1.
[12] Fadavi P, Nafisi N, Hariri R, et al. Serum Ferritin, Vitamin D and Pathological Factors in Breast Cancer Patients. Med J Islam Repub Iran. 2021; 35: 162. Published 2021 Dec 6. doi: 10.47176/mjiri.35.162.
[13] Alkhateeb AA, Han B, Connor JR. Ferritin stimulates breast cancer cells through an iron-independent mechanism and is localized within tumor-associated macrophages. Breast Cancer Res Treat. 2013; 137(3): 733-744. doi: 10.1007/s10549-012-2405-x.
[14] Duffy MJ, Shering S, Sherry F, et al. CA 15-3: a prognostic marker in breast cancer. Int J Biol Markers. 2000; 15(4): 330-333. doi: 10.1177/172460080001500410.
[15] Wojtacki J, Kruszewski WJ, Sliwińska M, et al. Elevation of serum Ca 15-3 antigen: an early indicator of distant metastasis from breast cancer. Retrospective analysis of 733 cases. Przegl Lek. 2001; 58(6): 498-503.
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[17] Nakata B, Takashima T, Ogawa Y, et al. Serum CYFRA 21-1 (cytokeratin-19 fragments) is a useful tumour marker for detecting disease relapse and assessing treatment efficacy in breast cancer. Br J Cancer. 2004; 91(5): 873-878. doi: 10.1038/sj.bjc.6602074.
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Cite This Article
  • APA Style

    Liu, Z., Lin, L., Zhu, G., Qiu, L. (2024). Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer. Clinical Medicine Research, 13(1), 6-12. https://doi.org/10.11648/j.cmr.20241301.12

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    ACS Style

    Liu, Z.; Lin, L.; Zhu, G.; Qiu, L. Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer. Clin. Med. Res. 2024, 13(1), 6-12. doi: 10.11648/j.cmr.20241301.12

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    AMA Style

    Liu Z, Lin L, Zhu G, Qiu L. Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer. Clin Med Res. 2024;13(1):6-12. doi: 10.11648/j.cmr.20241301.12

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  • @article{10.11648/j.cmr.20241301.12,
      author = {Zebin Liu and Lipeng Lin and Guosheng Zhu and Liying Qiu},
      title = {Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer},
      journal = {Clinical Medicine Research},
      volume = {13},
      number = {1},
      pages = {6-12},
      doi = {10.11648/j.cmr.20241301.12},
      url = {https://doi.org/10.11648/j.cmr.20241301.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20241301.12},
      abstract = {Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer
    },
     year = {2024}
    }
    

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    AU  - Zebin Liu
    AU  - Lipeng Lin
    AU  - Guosheng Zhu
    AU  - Liying Qiu
    Y1  - 2024/01/08
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    DO  - 10.11648/j.cmr.20241301.12
    T2  - Clinical Medicine Research
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    JO  - Clinical Medicine Research
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    AB  - Combining Multiple Tumor Markers to Construct a Clinical Prediction Model for Breast Cancer
    
    VL  - 13
    IS  - 1
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Author Information
  • Laboratory Department, Shenzhen Futian District Maternity and Child Healthcare Hospital, Shenzhen, China

  • Laboratory Department, Shenzhen Futian District Maternity and Child Healthcare Hospital, Shenzhen, China

  • Laboratory Department, Shenzhen Futian District Maternity and Child Healthcare Hospital, Shenzhen, China

  • Laboratory Department, Shenzhen Futian District Maternity and Child Healthcare Hospital, Shenzhen, China

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