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Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters

Crossref DOI link: https://doi.org/10.1007/s11547-024-01919-3

Published Online: 2024-11-14

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Luo, Tianchen

Yan, Meng

Zhou, Meng

Dekker, Andre

Appelt, Ane L.

Ji, Yongling

Zhu, Ji

de Ruysscher, Dirk

Wee, Leonard

Zhao, Lujun

Zhang, Zhen https://orcid.org/0000-0001-6335-9529
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (No. 81903121)
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Article History

Received: 19 May 2024

Accepted: 29 October 2024

First Online: 14 November 2024

Declarations

:

: The authors have no relevant financial or non-financial interests to disclose.

: This study is a retrospective analysis and the informed consents were waived after review and approval by the ethics committee.

: Each author gives consent for publication of the manuscript.

: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tianjin Medical University Cancer Institute & Hospital (Date: September 7, 2021/No: IRB/bc2021135).

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