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Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.

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Title: Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.
Authors: Cui M; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Zeng Z; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China., Chen S; Department of Radiology, Seventh Medical Center, Chinese PLA General Hospital, Beijing, China., Wang H; Radiology Department, Peking University First Hospital, Beijing, China., Jiang J; Department of Radiology, Beijing Friendship Hospital, Beijing, China., Cao Y; Radiology Department, Peking University First Hospital, Beijing, China., Ding X; Department of Pathology, The First Medical Center, Chinese PLA General Hospital, Beijing, China., Xu W; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Zhao T; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China., Zhao J; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Bai X; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Kang H; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Hao Y; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., Li L; Department of Innovative Medical Research, Chinese PLA General Hospital, Beijing, China., Yang D; Department of Radiology, Beijing Friendship Hospital, Beijing, China., Ye H; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China., He Y; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China., Wang H; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China. wanghaiyi301@outlook.com.
Source: Abdominal radiology (New York) [Abdom Radiol (NY)] 2026 May; Vol. 51 (5), pp. 2642-2653. Date of Electronic Publication: 2025 Oct 31.
Publication Type: Journal Article; Multicenter Study
Journal Info: Publisher: Springer Country of Publication: United States NLM ID: 101674571 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-0058 (Electronic) NLM ISO Abbreviation: Abdom Radiol (NY) Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.
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  Data: <searchLink fieldCode="AU" term="%22Cui+M%22">Cui M</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Zeng+Z%22">Zeng Z</searchLink>; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Chen+S%22">Chen S</searchLink>; Department of Radiology, Seventh Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+H%22">Wang H</searchLink>; Radiology Department, Peking University First Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Jiang+J%22">Jiang J</searchLink>; Department of Radiology, Beijing Friendship Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Cao+Y%22">Cao Y</searchLink>; Radiology Department, Peking University First Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Ding+X%22">Ding X</searchLink>; Department of Pathology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Xu+W%22">Xu W</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Zhao+T%22">Zhao T</searchLink>; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Zhao+J%22">Zhao J</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Bai+X%22">Bai X</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Kang+H%22">Kang H</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Hao+Y%22">Hao Y</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+L%22">Li L</searchLink>; Department of Innovative Medical Research, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yang+D%22">Yang D</searchLink>; Department of Radiology, Beijing Friendship Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Ye+H%22">Ye H</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22He+Y%22">He Y</searchLink>; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+H%22">Wang H</searchLink>; Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China. wanghaiyi301@outlook.com.
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  Data: <searchLink fieldCode="JN" term="%22101674571%22">Abdominal radiology (New York)</searchLink> [Abdom Radiol (NY)] 2026 May; Vol. 51 (5), pp. 2642-2653. <i>Date of Electronic Publication: </i>2025 Oct 31.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer%22">Springer </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101674571 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2366-0058 (Electronic) <i>NLM ISO Abbreviation: </i>Abdom Radiol (NY) <i>Subsets: </i>MEDLINE
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              Text: 2026 May
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