Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.
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| Title: | Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation. |
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| Authors: | Lee JO; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY., Kim DH; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea., Chae HD; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea., Lee E; College of Medicine, Seoul National University, Seoul, Republic of Korea.; Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea., Kang JH; Department of Radiology, Konkuk University Medical Center, Seoul, Republic of Korea., Lee JH; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea., Kim HJ; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea., Seo J; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea., Chai JW; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea. |
| Source: | Radiology. Artificial intelligence [Radiol Artif Intell] 2026 May; Vol. 8 (3), pp. e250283. |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Radiological Society of North America, Inc Country of Publication: United States NLM ID: 101746556 Publication Model: Print Cited Medium: Internet ISSN: 2638-6100 (Electronic) Linking ISSN: 26386100 NLM ISO Abbreviation: Radiol Artif Intell Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 41879561 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Lee+JO%22">Lee JO</searchLink>; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY.<br /><searchLink fieldCode="AU" term="%22Kim+DH%22">Kim DH</searchLink>; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Chae+HD%22">Chae HD</searchLink>; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+E%22">Lee E</searchLink>; College of Medicine, Seoul National University, Seoul, Republic of Korea.; Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kang+JH%22">Kang JH</searchLink>; Department of Radiology, Konkuk University Medical Center, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lee+JH%22">Lee JH</searchLink>; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+HJ%22">Kim HJ</searchLink>; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Seo+J%22">Seo J</searchLink>; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Chai+JW%22">Chai JW</searchLink>; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University, College of Medicine, 20 Boramae-ro 5-gil, Dongjak-gu, Seoul, 07061, Republic of Korea.; College of Medicine, Seoul National University, Seoul, Republic of Korea. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101746556%22">Radiology. Artificial intelligence</searchLink> [Radiol Artif Intell] 2026 May; Vol. 8 (3), pp. e250283. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Multicenter Study – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Radiological+Society+of+North+America%2C+Inc%22">Radiological Society of North America, Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101746556 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>2638-6100 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2226386100%22">26386100 </searchLink><i>NLM ISO Abbreviation: </i>Radiol Artif Intell <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=41879561 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1148/ryai.250283 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: e250283 Titles: – TitleFull: Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee JO – PersonEntity: Name: NameFull: Kim DH – PersonEntity: Name: NameFull: Chae HD – PersonEntity: Name: NameFull: Lee E – PersonEntity: Name: NameFull: Kang JH – PersonEntity: Name: NameFull: Lee JH – PersonEntity: Name: NameFull: Kim HJ – PersonEntity: Name: NameFull: Seo J – PersonEntity: Name: NameFull: Chai JW IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 May Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2638-6100 Numbering: – Type: volume Value: 8 – Type: issue Value: 3 Titles: – TitleFull: Radiology. Artificial intelligence Type: main |
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