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An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy

Crossref DOI link: https://doi.org/10.1186/s12938-025-01460-y

Published Online: 2025-10-21

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

Authors

Zeng, Shengtao

Cao, Zhi

Xu, Haoxiang

Yang, Chenglin

Wang, Kaiqiang

Yang, Yue

Qiu, Xiaofu

Xiao, Yuansong

Zhang, Xiaoming

Fu, Qiangfeng

Wang, Wei
Funding

Funding for this research was provided by:

Guangzhou Municipal Science and Technology Project (No. 202002030030)
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Text and Data Mining valid from 2025-10-21

Version of Record valid from 2025-10-21
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Article History

Received: 17 April 2025

Accepted: 19 September 2025

First Online: 21 October 2025

Declarations

:

: This study followed the principles of the Helsinki Declaration (revised in 2013) and was approved by the Ethics Committee of the General Hospital of Southern Theater Command (No. NZLLKZ2023159) and the Ethics Committee of the Guangdong Second Provincial General Hospital (No. 2023-KY-KZ-263-02).

: All contributors have critically evaluated the content, validated its scholarly rigor, and consented to publication in accordance with International Committee of Medical Journal Editors (ICMJE) guidelines. Accountability for the work’s validity is collectively affirmed.

: The authors declare no competing interests.

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