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Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women

Crossref DOI link: https://doi.org/10.1038/s41598-025-21894-7

Published Online: 2025-10-30

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

Authors

Zheng, Bin

Yu, Panfeng

Zhu, Zhenqi

Liang, Yan

Liu, Haiying
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Text and Data Mining valid from 2025-10-30

Version of Record valid from 2025-10-30
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Article History

Received: 19 July 2025

Accepted: 24 September 2025

First Online: 30 October 2025

Declarations

:

: The authors declare no competing interests.

: This retrospective study is approved by the Institutional Review Board (IRB) of Peking University People’s Hospital. This study complies with all principles and regulations of the Helsinki Declaration. All patients involved in the study consent to participate in the study. And written informed consent has been obtained from all the patients.

: All individual person consented for their data to be published.

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