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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.

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Title: 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.
Authors: Liu, Lu1 (AUTHOR), Cai, Wenjun2 (AUTHOR), Zheng, Feibo3 (AUTHOR), Tian, Hongyan1 (AUTHOR), Li, Yanping1 (AUTHOR), Wang, Ting1 (AUTHOR), Chen, Xiaonan4 (AUTHOR) chenxn@cmu.edu.cn, Zhu, Wenjing5 (AUTHOR) zhuwj@uor.edu.cn
Source: Insights into Imaging. 1/13/2025, Vol. 16 Issue 1, p1-15. 15p.
Database: Academic Search Ultimate
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Header DbId: asn
DbLabel: Academic Search Ultimate
An: 183072105
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: 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.
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  Data: <searchLink fieldCode="JN" term="%22Insights+into+Imaging%22">Insights into Imaging</searchLink>. 1/13/2025, Vol. 16 Issue 1, p1-15. 15p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=183072105
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1186/s13244-024-01874-7
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      – Code: eng
        Text: English
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        PageCount: 15
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      – TitleFull: 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.
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            NameFull: Liu, Lu
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            NameFull: Cai, Wenjun
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            NameFull: Zheng, Feibo
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            NameFull: Tian, Hongyan
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            NameFull: Li, Yanping
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            NameFull: Wang, Ting
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            – D: 13
              M: 01
              Text: 1/13/2025
              Type: published
              Y: 2025
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            – TitleFull: Insights into Imaging
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