Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

PURPOSE: To establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical param...

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Detalles Bibliográficos
Autores principales: Peng, Tao, Xiao, JianMing, Li, Lin, Pu, BingJie, Niu, XiangKe, Zeng, XiaoHui, Wang, ZongYong, Gao, ChaoBang, Li, Ci, Chen, Lin, Yang, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616865/
https://www.ncbi.nlm.nih.gov/pubmed/34677748
http://dx.doi.org/10.1007/s11548-021-02507-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616865/
https://www.ncbi.nlm.nih.gov/pubmed/34677748
http://dx.doi.org/10.1007/s11548-021-02507-w

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