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The CT-based deep learning model outperforms traditional anatomical classification models in preoperatively predicting complications and risk grade in partial nephrectomy

Crossref DOI link: https://doi.org/10.1007/s00345-025-05893-x

Published Online: 2025-10-25

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

Authors

Du, Lingzhi

Cheng, Jie

Shen, Chenyang

Cheng, Jiangting

Yang, Guanwen

He, Chang

Xu, Peirong

Lin, Wenyao

Liu, Li

Hu, Xiaoyi

Huang, Jiaqi

Pang, Yangyang

Xu, Ganzhe

Guo, Jianming

Zhu, Yanjun

Wang, Hang
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (No. 62273099, 82203517)

Natural Science Foundation of Shanghai Municipality (No. 22ZR1458000)

Shanghai Association for Science and Technology (No. 22Y11905300)

Double First Class University Plan (No. IDF152136)
License Information

Text and Data Mining valid from 2025-10-25

Version of Record valid from 2025-10-25
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Article History

Received: 12 June 2025

Accepted: 15 August 2025

First Online: 25 October 2025

Declarations

:

: The authors declare that they have no conflict of interest. All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

: The ethical approval was exempted because the study was retrospective and no interventions were made on the patients.

: The requirement for obtaining informed consent from the patients was waived due to the retrospective design.

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