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Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.

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Title: Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.
Authors: Yuan Z; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Mei Y; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Peng X; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Wei Z; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Xv Y; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Xiao B; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China., Xiao M; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Source: Frontiers in oncology [Front Oncol] 2026 Jun 23; Vol. 16, pp. 1844559. Date of Electronic Publication: 2026 Jun 23 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101568867 Publication Model: eCollection Cited Medium: Print ISSN: 2234-943X (Print) Linking ISSN: 2234943X NLM ISO Abbreviation: Front Oncol Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.
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  Data: <searchLink fieldCode="AU" term="%22Yuan+Z%22">Yuan Z</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Mei+Y%22">Mei Y</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Peng+X%22">Peng X</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Wei+Z%22">Wei Z</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xv+Y%22">Xv Y</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xiao+B%22">Xiao B</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.<br /><searchLink fieldCode="AU" term="%22Xiao+M%22">Xiao M</searchLink>; Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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  Data: <searchLink fieldCode="JN" term="%22101568867%22">Frontiers in oncology</searchLink> [Front Oncol] 2026 Jun 23; Vol. 16, pp. 1844559. <i>Date of Electronic Publication: </i>2026 Jun 23 (<i>Print Publication: </i>2026).
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        Value: 10.3389/fonc.2026.1844559
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      – TitleFull: Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.
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              Text: 2026 Jun 23
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