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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses

Crossref DOI link: https://doi.org/10.1186/s13244-024-01874-7

Published Online: 2025-01-13

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

Authors

Liu, Lu

Cai, Wenjun

Zheng, Feibo

Tian, Hongyan

Li, Yanping

Wang, Ting

Chen, Xiaonan

Zhu, Wenjing https://orcid.org/0000-0003-0958-0905
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Text and Data Mining valid from 2025-01-13

Version of Record valid from 2025-01-13
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Article History

Received: 10 October 2024

Accepted: 28 November 2024

First Online: 13 January 2025

Declarations

:

: The study was conducted in accordance with the Declaration of Helsinki and approved by the ethical committees of the South China Hospital of Shenzhen University (approval number: HNLS20230112101-A). Given the study’s retrospective nature, the requirement for patient-informed consent was granted an exemption.

: Not applicable.

: The authors declare that they have no competing interests.

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