[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Predicting the Risk of Metastatic Dissemination in Non-small Cell Lung Cancer Using Clinical and Genetic Data

  • Conference paper
  • First Online:
The Latest Developments and Challenges in Biomedical Engineering (PCBEE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 746))

Included in the following conference series:

  • 166 Accesses

Abstract

The major cause of cancer patient death is the development of distant tumors (metastases) that leads to a disease that is hard to treat. Therefore, the emergence of metastasis is a key time-point in the management of cancer leading to a decision about more intensive treatment. One of the most metastatic cancer types is lung cancer, in which the most common subtype is non-small cell lung cancer (NSCLC). We selected a set of common genetic variants in 27 genes and identified them in DNA from the peripheral blood of 335 NSCLC patients. Our goal is to apply a predictive machine learning model to estimate the risk of metastasis based on clinical and SNPs data. We have found that Cox regression with LASSO regularization gives the best predictive accuracy with a concordance index equal to 0.63.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 127.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 159.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Barta JA, Powell CA, Wisnivesky JP (2019) Global Epidemiology of Lung T. https://doi.org/10.5334/aogh.2419

  2. Bennett, S.: Log-logistic regression models for survival data. Appl. Stat. 32(2), 165 (1983). issn: 00359254. https://doi.org/10.2307/2347295

  3. Bühlmann, P., et al.: Model-based Boosting 2.0. J. Mach. Learn. Res. 11(71), 2109–2113 (2010). issn: 1533-7928. http://jmlr.org/papers/v11/hothorn10a.html

  4. Butkiewicz, D., et al.: Polymorphisms in egfr gene predict clinical outcome in unresectable non-small cell lung cancer treated with radiotherapy and platinum-based chemoradiotherapy. Int. J. Mol. Sci. 22(11), 5605 (2021). issn: 14220067. https://doi.org/10.3390/IJMS22115605/S1, https://www.mdpi.com/1422-0067/ 22/11/5605/htm

  5. ECOG Performance Status Scale—ECOG-ACRIN Cancer Research Group. https://ecog-acrin.org/resources/ecog-performance-status/. Visited 05 Nov 2023

  6. Harrell, F.E., Lee, K.L., Mark, D.B.: Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361–387 (1996). issn: 02776715. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4\(<\)361::AID-SIM168\(>\) 3.0.CO;2-4, https://onlinelibrary.wiley.com/doi/epdf/10.1002/

    Google Scholar 

  7. Hothorn, T., Bühlmann, P.: Model-based boosting in high dimensions. Bioinformatics 22(22), 2828–2829 (2006). issn: 1367-4803. https://doi.org/10.1093/BIOINFORMATICS/BTL462, https://academic.oup.com/bioinformatics/article/22/22/2828/197039

  8. Royston, P.: The lognormal distribution as a model for survival time in cancer, with an emphasis on prognostic factors. Statistica Neerlandica 55(1), 89–104 (2001). issn: 1467-9574. https://doi.org/10.1111/1467-9574.00158, https://onlinelibrary.wiley.com/doi/full/10.1111/14679574.00158

  9. Simon, N., et al.: Regularization paths for cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39(5), 1–13 (2011). issn: 1548-7660. https://doi.org/10.18637/JSS.V039.I05, https://www.jstatsoft.org/index.php/jss/article/view/v039i05

  10. Taketomi, N., et al.: Parametric distributions for survival and reliability analyses, a review and historical sketch. Mathematics 10(20), 3907 (2022). issn: 22277390. https://doi.org/10.3390/MATH10203907/S1, https://www.mdpi.com/2227-7390/10/20/3907/htm

Download references

Acknowledgements

This research was funded by the National Science Centre (NCN), Poland, grant number 2012/05/B/NZ5/01905 (to D.B.) and 2020/37/B/ST6/01959 (to A. S., E. K., A. M. W.).

Author information

Authors and Affiliations

  1. Silesian University of Technology, Department of Systems Biology and Engineering, 44-102, Gliwice, Poland

    Emilia Kozłowska, Agata Małgorzata Wilk & Andrzej Świerniak

  2. Department of Biostatistics and Bioinformatics, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102, Gliwice, Poland

    Agata Małgorzata Wilk

  3. Center for Translational Research and Molecular Biology of Cancer, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102, Gliwice, Poland

    Dorota Butkiewicz, Małgorzata Krześniak & Agnieszka Gdowicz-Kłosok

  4. II-nd Radiotherapy and Chemotherapy Clinic and Teaching Hospital, Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102, Gliwice, Poland

    Monika Giglok & Rafał Suwiński

Authors
  1. Emilia Kozłowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agata Małgorzata Wilk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dorota Butkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Małgorzata Krześniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Agnieszka Gdowicz-Kłosok
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Monika Giglok
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rafał Suwiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andrzej Świerniak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emilia Kozłowska .

Editor information

Editors and Affiliations

  1. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Paweł Strumiłło

  2. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Artur Klepaczko

  3. Institute of Electronics, Lodz University of Technology, Lodz, Poland

    Michał Strzelecki

  4. Institute of Materials Science, Lodz University of Technology, Lodz, Poland

    Dorota Bociąga

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kozłowska, E. et al. (2024). Predicting the Risk of Metastatic Dissemination in Non-small Cell Lung Cancer Using Clinical and Genetic Data. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-38430-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38429-5

  • Online ISBN: 978-3-031-38430-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation