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Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

Crossref DOI link: https://doi.org/10.1186/s12880-024-01251-2

Published Online: 2024-04-15

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

Authors

Du, Yangchun

Guo, Wenwen

Xiao, Yanju

Chen, Haining

Yao, Jinxiu

Wu, Ji
Funding

Funding for this research was provided by:

The Guangxi Promotion of Appropriate Health Technologies Project (S2021055)

The Natural Science Foundation of Guangxi Zhuang Autonomous Region (2023GXNSFDA026010)

The Guangxi Medical”139” Project for Training High-level Backbone Talents (G201903014)
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Text and Data Mining valid from 2024-04-15

Version of Record valid from 2024-04-15
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Article History

Received: 6 September 2023

Accepted: 18 March 2024

First Online: 15 April 2024

Declarations

:

: This retrospective study was approved by the ethics committee of the People’s Hospital of Guangxi Zhuang Autonomous Region (KY-SY-2021-21) and waived the requirement for informed consent. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

: Not applicable.

: The authors declare no competing interests.

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