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Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease

  • Débora Martínez-Espartosa ORCID logo , Estíbaliz Alegre , Hugo Casero-Ramírez , Jorge Díaz-Garzón ORCID logo , Pilar Fernández-Calle , Patricia Fuentes-Bullejos ORCID logo , Nerea Varo and Álvaro González EMAIL logo
Published/Copyright: August 6, 2024

Abstract

Objectives

Personalized reference intervals (prRI) have been proposed as a diagnostic tool for assessing measurands with high individuality. Here, we evaluate clinical performance of prRI using carcinoembryonic antigen (CEA) for cancer detection and compare it with that of reference change values (RCV) and other criteria recommended by clinical guidelines (e.g. 25 % of change between consecutive CEA results (RV25) and the cut-off point of 5 μg/L (CP5)).

Methods

Clinical and analytical data from 2,638 patients collected over 19 years were retrospectively evaluated. A total 15,485 CEA results were studied. For each patient, we calculated prRI and RCV using computer algorithms based on the combination of different strategies to assess the number of CEA results needed, consideration of one or two limits of reference interval and the intraindividual biological variation estimate (CVI) used: (a) publicly available (CVI-EU), (b) CVI calculated using an indirect method (CVI-NOO) and (c) within-person BV (CVP). For each new result identified falling outside the prRI, exceeding the RCV interval, RV25 or CP5, we searched for records identifying the presence of tumour at 3 and 12 months after the test. The sensitivity, specificity and predictive power of each strategy were calculated.

Results

PrRI approaches derived using CVI-EU, and both limits of reference interval achieve the best sensitivity (87.5 %) and NPV (99.3 %) at 3 and 12 months of all evaluated criteria. Only 3 results per patients are enough to calculate prRIs that reach this diagnostic performance.

Conclusions

PrRI approaches could be an effective tool to rule out new oncological findings during the active surveillance of patients.


Corresponding author: Álvaro González, Biochemistry Department, Clínica Universidad de Navarra, Pamplona, Spain; and Navarra Institute for Health Research (IDISNA), 31008 Pamplona, Spain, E-mail:
Nerea Varo and Álvaro González contributed equally to this work.

Acknowledgments

We would like to thank Dra. María Romero for her support in the preparation of the manuscript.

  1. Research ethics: This study was approved by the Research Ethics Committee of the University of Navarra (2023-041) in agreement with the World Medical Association Declaration of Helsinki and the Spanish law.

  2. Informed consent: Not applicable.

  3. Author contributions: A.E and V.N collected the data, M-E.D, A.E, V.N and G.A concieved and designed the analysis, M-E.D and F-B.P performed the analysis, C-R.H contributed analysis tools, M-E.D, A.E, V.N, G.A D-G.J and F-C.P wrote the paper and revised the article for intellectual content. All authors discussed the results and contributed to the final manuscript.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: The datasets generated during the current study are available from the corresponding author on reasonable request.

References

1. Hing, JX, Mok, CW, Tan, PT, Sudhakar, SS, Seah, CM, Lee, WP, et al.. Clinical utility of tumour marker velocity of cancer antigen 15-3 (CA 15-3) and carcinoembryonic antigen (CEA) in breast cancer surveillance. Breast 2020;52:95–101. https://doi.org/10.1016/j.breast.2020.05.005.Search in Google Scholar PubMed PubMed Central

2. Gaspar-Blázquez, JM, Pujol-Jaume, T, Pradera-Augé, JM, Barco-Sánchez, A, Carbonell-Muñoz, R, Filella-Pla, X, et al.. Recomendaciones para la optimización del uso de marcadores tumorales. Rev Lab Clin 2019;12:38–52. https://doi.org/10.1016/j.labcli.2018.09.002.Search in Google Scholar

3. Molina, R, Barak, V, Van Dalen, A, Duffy, MJ, Einarsson, R, Gion, M, et al.. Tumor markers in breast cancer – European group on tumor markers recommendations. Tumor Biol 2005;26:281–93. https://doi.org/10.1159/000089260.Search in Google Scholar PubMed

4. Wells, SA, Asa, SL, Dralle, H, Elisei, R, Evans, DB, Gagel, RF, et al.. Revised American thyroid association guidelines for the management of medullary thyroid carcinoma. Thyroid 2015;25:567–610. https://doi.org/10.1089/thy.2014.0335.Search in Google Scholar PubMed PubMed Central

5. Tuxen, MK, Sölétormos, G, Dombernowsky, P. Tumor markers in the management of patients with ovarian cancer. Cancer Treat Rev 1995;21:215–45. https://doi.org/10.1016/0305-7372(95)90002-0.Search in Google Scholar PubMed

6. Duffy, MJ, Van Dalen, A, Haglund, C, Hansson, L, Klapdor, R, Lamerz, R, et al.. Clinical utility of biochemical markers in colorectal cancer: European Group on Tumour Markers (EGTM) guidelines. Eur J Cancer 2003;39:718–27. https://doi.org/10.1016/s0959-8049(02)00811-0.Search in Google Scholar PubMed

7. Locker, GY, Hamilton, S, Harris, J, Jessup, JM, Kemeny, N, Macdonald, JS, et al.. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin Oncol 2006;24:5313–27. https://doi.org/10.1200/jco.2006.08.2644.Search in Google Scholar

8. Lin, YH, Wu, CH, Fu, HC, Chen, YJ, Chen, YY, Ou, YC, et al.. Prognostic significance of elevated pretreatment serum levels of CEA and CA-125 in epithelial ovarian cancer. Cancer Biomarkers 2020;28:285–92. https://doi.org/10.3233/cbm-201455.Search in Google Scholar PubMed

9. Grunnet, M, Sorensen, JB. Lung Cancer Carcinoembryonic antigen ( CEA ) as tumor marker in lung cancer. Lung Cancer 2012;76:138–43. https://doi.org/10.1016/j.lungcan.2011.11.012.Search in Google Scholar PubMed

10. Bozkurt-Yavuz, H, Akif-Bildirici, M, Yaman, H, Caner-Karahan, S, Aliyazıcıoğlu, Y, Örem, A. Reference change value and measurement uncertainty in the evaluation of tumor markers. Scand J Clin Lab Invest 2021;81:601–5. https://doi.org/10.1080/00365513.2021.1979244.Search in Google Scholar PubMed

11. Cartei, G, Cartei, F, Bertin, M, Padoan, A, Zustovich, F, Ornella Nicoletto, M, et al.. CA125 reference values change in male and postmenopausal female subjects. Clin Chem Lab Med 2013;51:413–9. https://doi.org/10.1515/cclm-2012-0414.Search in Google Scholar PubMed

12. Trapé, J, Botargues, JM, Porta, F, Ricós, C, Badal, JM, Salinas, R, et al.. Reference change value for alpha-fetoprotein and its application in early detection of hepatocellular carcinoma in patients with hepatic disease. Clin Chem 2003;49:1209–11. https://doi.org/10.1373/49.7.1209.Search in Google Scholar PubMed

13. Van Rossum, HH, Meng, QH, Ramanathan, LV, Holdenrieder, S. A word of caution on using tumor biomarker reference change values to guide medical decisions and the need for alternatives. Clin Chem Lab Med 2022;60:553–5. https://doi.org/10.1515/cclm-2021-0933.Search in Google Scholar PubMed

14. Lund, F, Petersen, PH, Fraser, CG. A dynamic reference change value model applied to ongoing assessment of the steady state of a biomarker using more than two serial results. Ann Clin Biochem 2019;56:283–94. https://doi.org/10.1177/0004563219826168.Search in Google Scholar PubMed

15. Lund, F, Petersen, PH, Fraser, CG, Sölétormos, G. Calculation of limits for significant bidirectional changes in two or more serial results of a biomarker based on a computer simulation model. Ann Clin Biochem 2015;52:434–40. https://doi.org/10.1177/0004563214555163.Search in Google Scholar PubMed

16. Lund, F, Petersen, PH, Fraser, CG, Sölétormos, G. Calculation of limits for significant unidirectional changes in two or more serial results of a biomarker based on a computer simulation model. Ann Clin Biochem 2015;52:237–44. https://doi.org/10.1177/0004563214534636.Search in Google Scholar PubMed

17. Lund, F, Petersen, PH, Fraser, CG, Sölétormos, G. Different percentages of false-positive results obtained using five methods for the calculation of reference change values based on simulated normal and ln-normal distributions of data. Ann Clin Biochem 2016;53:692–8. https://doi.org/10.1177/0004563216643729.Search in Google Scholar PubMed

18. Coşkun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals in laboratory medicine: a new model based on within-subject biological variation. Clin Chem 2021;67:374–84. https://doi.org/10.1093/clinchem/hvaa233.Search in Google Scholar PubMed

19. Coskun, A, Sandberg, S, Unsal, I, Yavuz, FG, Cavusoglu, C, Serteser, M, et al.. Personalized reference intervals - statistical approaches and considerations. Clin Chem Lab Med 2022;60:629–35. https://doi.org/10.1515/cclm-2021-1066.Search in Google Scholar PubMed

20. Coskun, A, Sandberg, S, Unsal, I, Serteser, M, Aarsand, AK. Personalized reference intervals: from theory to practice. Crit Rev Clin Lab Sci 2022;59:501–16. https://doi.org/10.1080/10408363.2022.2070905.Search in Google Scholar PubMed

21. Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Personalized reference intervals: from the statistical significance to the clinical usefulness. Clin Chim Acta 2022;524:203–4. https://doi.org/10.1016/j.cca.2021.10.036.Search in Google Scholar PubMed

22. Tuxen, MK, Sölétormos, G, Petersen, PH, Schiøler, V, Dombernowsky, P. Assessment of biological variation and analytical imprecision of CA 125, CEA, and TPA in relation to monitoring of ovarian cancer. Gynecol Oncol 1999;74:12–22. https://doi.org/10.1006/gyno.1999.5414.Search in Google Scholar PubMed

23. Coşkun, A, Aarsand, AK, Sandberg, S, Guerra, E, Locatelli, M, Díaz-Garzón, J, et al.. Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study. Clin Chem Lab Med 2022;60:543–52. https://doi.org/10.1515/cclm-2021-0283.Search in Google Scholar PubMed

24. Erden, G, Barazi, AO, Tezcan, G, Yildirimkaya, MM. Biological variation and reference change values of CA 19-9, CEA, AFP in serum of healthy individuals. Scand J Clin Lab Invest 2008;68:212–8. https://doi.org/10.1080/00365510701601699.Search in Google Scholar PubMed

25. Coskun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals: using estimates of within-subject or within-person biological variation requires different statistical approaches. Clin Chim Acta 2022;524:201–2. https://doi.org/10.1016/j.cca.2021.10.034.Search in Google Scholar PubMed

26. Wang, S, Zhao, M, Su, Z, Mu, R. Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes. Clin Chem Lab Med 2022;60:606–17. https://doi.org/10.1515/cclm-2021-0479.Search in Google Scholar PubMed

27. Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Within-person biological variation estimates from the European Biological Variation Study (EuBIVAS) for serum potassium and creatinine used to obtain personalized reference intervals. Clin Chim Acta 2021;523:205–7. https://doi.org/10.1016/j.cca.2021.09.018.Search in Google Scholar PubMed

28. Coşkun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized and population-based reference intervals for 48 common clinical chemistry and hematology measurands: a comparative study. Clin Chem 2023;69:1009–30. https://doi.org/10.1093/clinchem/hvad113.Search in Google Scholar PubMed

29. Aarsand, A, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, et al.. The EFLM biological variation database [Internet]. Available from: https://biologicalvariation.eu/ [Accessed 26 Apr 2024].Search in Google Scholar

30. Marqués-García, F, Nieto-Librero, A, González-García, N, Galindo-Villardón, P, Martínez-Sánchez, LM, Tejedor-Ganduxé, X, et al.. Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa). Clin Chem Lab Med 2022;60:1804–12. https://doi.org/10.1515/cclm-2021-0863.Search in Google Scholar PubMed

31. Coskun, A, Lippi, G. Personalized laboratory medicine in the digital health era: recent developments and future challenges. Clin Chem Lab Med 2023;62:402–9. https://doi.org/10.1515/cclm-2023-0808.Search in Google Scholar PubMed

32. Fokkema, M, Herrmann, Z, Muskiet, FAJ, Moecks, J. Reference change values for brain natriuretic peptides revisited. Clin Chem 2006;52:1602–3. https://doi.org/10.1373/clinchem.2006.069369.Search in Google Scholar PubMed

33. Virtanen, P, Gommers, R, Oliphant, TE, Haberland, M, Reddy, T, Cournapeau, D, et al.. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17:261–72. https://doi.org/10.1038/s41592-019-0686-2.Search in Google Scholar PubMed PubMed Central

34. Harris, CR, Millman, KJ, van der Walt, SJ, Gommers, R, Virtanen, P, Cournapeau, D, et al.. Array programming with NumPy. Nature 2020;585:357–62. https://doi.org/10.1038/s41586-020-2649-2.Search in Google Scholar PubMed PubMed Central

35. Python Software Foundation. Python language reference. [Internet]. versión 3. Available from: http://www.python.org.Search in Google Scholar

36. McKinney, W. Data structures for statistical computing in Python. In: Proceedings of the 9th Python in Science Conference. Austin; 2010:51–6 pp.10.25080/Majora-92bf1922-00aSearch in Google Scholar

37. Loh, TP, Ranieri, E, Metz, MP. Derivation of pediatric within-individual biological variation by indirect sampling method. Am J Clin Pathol 2014;142:657–63. https://doi.org/10.1309/ajcphzlqaeyh94hi.Search in Google Scholar PubMed

38. Dallas-Jones, GR. Estimates of within-subject biological variation derived from pathology databases: an approach to allow assessment of the effects of age, sex, time between sample collections, and analyte concentration on reference change values. Clin Chem 2019;65:579–88. https://doi.org/10.1373/clinchem.2018.290841.Search in Google Scholar PubMed


Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0546).


Received: 2024-04-30
Accepted: 2024-07-02
Published Online: 2024-08-06
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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