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Predicting Metastasis-Free Survival Using Clinical Data in Non-small Cell Lung Cancer

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1716))

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Abstract

Lung cancer is the most common and the deadliest type of cancer with 5-year overall survival equal to 15%. One of the main reasons for the high mortality of lung cancer is the development of local and distant metastases. Lung cancer patients mostly die because of distant metastases rather than the primary tumor. Thus, here we tackle the problem of predicting when a patient relapse with a distant metastatic tumor. This information is relevant not only to assess a patient’s prognosis but also to guide the first-line treatment. Here, we applied clinical data from over 400 patients to predict the time to metastatic relapse which is also called metastasis-free survival (MFS). Using Cox regression, we have got a fairly good prediction with a c-index = 0.63 for a model with three clinical covariates. In addition, we created also a nomogram that could be applied to predicting the probability of metastases in newly diagnosed patients. In conclusion, solely based on clinical data, it is possible to predict the time to metastasis.

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Acknowledgments

We would like to acknowledge the financial support of the National Science Center, Poland - grant number 2020/37/B/ST6/01959 and EMBO short fellowship.

The work was partially carried out during a research visit at Inria (Marseille, France). We thank the leader of the visiting laboratory – Sebastien Benzekry for fruitful discussion and valuable comments.

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Authors and Affiliations

  1. Department of Systems Biology and Engineering, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Emilia Kozłowska & Andrzej Świerniak

  2. The 2nd Radiotherapy and Chemotherapy Clinic, M. Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland

    Monika Giglok & Rafał Suwiński

  3. Department of Radiotherapy, M. Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland

    Iwona Dębosz-Suwińska

Authors
  1. Emilia Kozłowska
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  2. Monika Giglok
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  3. Iwona Dębosz-Suwińska
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  4. Rafał Suwiński
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  5. Andrzej Świerniak
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Corresponding authors

Correspondence to Emilia Kozłowska or Andrzej Świerniak .

Editor information

Editors and Affiliations

  1. University of Newcastle Australia, Newcastle, NSW, Australia

    Edward Szczerbicki

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. International University - VNU-HCM, Ho Chi Minh City, Vietnam

    Sinh Van Nguyen

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Pietranik

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Marek Krótkiewicz

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kozłowska, E., Giglok, M., Dębosz-Suwińska, I., Suwiński, R., Świerniak, A. (2022). Predicting Metastasis-Free Survival Using Clinical Data in Non-small Cell Lung Cancer. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_18

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  • DOI: https://doi.org/10.1007/978-981-19-8234-7_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8233-0

  • Online ISBN: 978-981-19-8234-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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