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 parame...

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Veröffentlicht in:	International journal for computer assisted radiology and surgery 2021-12, Vol.16 (12), p.2235-2249
Hauptverfasser:	Peng, Tao, Xiao, JianMing, Li, Lin, Pu, BingJie, Niu, XiangKe, Zeng, XiaoHui, Wang, ZongYong, Gao, ChaoBang, Li, Ci, Chen, Lin, Yang, Jin
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification Clinical significance Computer Imaging Computer Science Datasets Decision trees Diagnosis Diffusion coefficient Health Informatics Humans Imaging Machine Learning Magnetic Resonance Imaging Male Mathematical models Medical imaging Medicine Medicine & Public Health Original Original Article Parameters Pattern Recognition and Graphics Performance enhancement Prostate cancer Prostatic Neoplasms - diagnostic imaging Radiology Retrospective Studies Sensitivity Stability analysis Support vector machines Surgery Texture Vision
Online-Zugang:	Volltext
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