Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models
The studied methods are highly relevant for epidemiological researchers to create more accurate survival models, which can help physicians make informed decisions about appropriate therapies and management of patients with lung cancer, ultimately improving survival and quality of life.
Authors
- Heinz Handels (ORCID: https://orcid.org/0000-0002-3499-4328)
- Bernd Holleczek
- Christiane Rudolph (ORCID: https://orcid.org/0000-0001-8294-2963)
- Louisa Labohm (ORCID: https://orcid.org/0009-0000-9631-4247)
- Alexander Katalinic (ORCID: https://orcid.org/0000-0003-0490-1554)
- Sebastian Germer (ORCID: https://orcid.org/0000-0001-8439-872X)
- Natalie Rath (ORCID: https://orcid.org/0009-0000-4818-7773)
- Katharina Rausch
Institutions
- German Research Centre for Artificial Intelligence (DE)
- Krebsregister Saarland (DE)
- University of Lübeck (DE)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105607
- Citations
- 19
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 7.68