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Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer

Crossref DOI link: https://doi.org/10.1186/s13058-026-02275-y

Published Online: 2026-04-04

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

Authors

Li, Zhiyong

Su, Huanzhong

Xiao, Han

Chen, Cong

Lin, Peng

Xue, Ensheng

Liang, Rongxi

Ye, Qin

Lin, Zhenhu
Funding

Funding for this research was provided by:

Joint Funds for the innovation of science and Technology, Fujian province (2024Y9259)

Fujian provincial health technology project (2024QNA021)
License Information

Text and Data Mining valid from 2026-04-04

Version of Record valid from 2026-05-19
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Article History

Received: 30 November 2025

Accepted: 30 March 2026

First Online: 4 April 2026

Declarations

:

: This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (approval number: 2024KJCX061, approval date: September 6, 2024). Due to the retrospective nature of this study, the requirement for informed consent was waived by the Ethics Committee of Fujian Medical University Union Hospital. All research procedures were conducted in accordance with the principles of the Declaration of Helsinki.

: Not applicable.

: The authors declare no competing interests.

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