Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI ; volume:14 ; number:1 ; day:12 ; month:1 ; year:2024 ; pages:1-11 ; date:12.2024
Scientific reports ; 14, Heft 1 (12.1.2024), 1-11, 12.2024
- Creator
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Jeong, Sungmoon
Yu, Hosang
Park, Shin-Hyung
Woo, Dongwon
Lee, Seoung-Jun
Chong, Gun Oh
Han, Hyung Soo
Kim, Jae-Chul
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-024-51742-z
- URN
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urn:nbn:de:101:1-2024032515392908231241
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:54 AM CEST
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Associated
- Jeong, Sungmoon
- Yu, Hosang
- Park, Shin-Hyung
- Woo, Dongwon
- Lee, Seoung-Jun
- Chong, Gun Oh
- Han, Hyung Soo
- Kim, Jae-Chul
- SpringerLink (Online service)
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KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients
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CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy
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Systemic immune-related spleen radiomics predict progression-free survival in patients with locally advanced cervical cancer underwent definitive chemoradiotherapy
A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer
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