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
Description
ISSN:2366-0058
DOI:10.1007/s00261-025-05259-2