Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

ObjectiveThe purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.Materials and methodsThe computed tomography (CT...

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Bibliographic Details
Main Authors: Zhikang Deng, Wentao Dong, Situ Xiong, Di Jin, Hongzhang Zhou, Ling Zhang, LiHan Xie, Yaohong Deng, Rong Xu, Bing Fan
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-05-01
Series:Frontiers in Oncology
Subjects:
bladder cancer
pathological grade
combined radiomics nomogram
textural features
non-enhanced computed tomography
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1166245/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2023.1166245/full

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