Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions

The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomog...

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Veröffentlicht in:	Magnetic resonance imaging Jg. 100; S. 64 - 72
Hauptverfasser:	Nai, Ying-Hwey, Cheong, Dennis Lai Hong, Roy, Sharmili, Kok, Trina, Stephenson, Mary C., Schaefferkoetter, Josh, Totman, John J., Conti, Maurizio, Eriksson, Lars, Robins, Edward G., Wang, Ziting, Chua, Wynne Yuru, Ang, Bertrand Wei Leng, Singha, Arvind Kumar, Thamboo, Thomas Paulraj, Chiong, Edmund, Reilhac, Anthonin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier Inc 01.07.2023
Schlagworte:	Gleason score (GS) Humans Machine Learning Machine learning (ML) Magnetic Resonance Imaging - methods Male Multiparametric magnetic resonance imaging (mpMRI) Neoplasm Grading Positron emission tomography (PET) Prostate-Specific Antigen Prostatic Neoplasms - pathology Quantitative parameters Radiomics Retrospective Studies PRC ADC TZ PZ PSADWP AUC GLDM DT DWI EP2D ML PSA DCE ROC TSE EM Gleason score (GS) ROI GLCM Ktrans SUV GRE GLRLM PSAD SUVmax SVM TOF-PSF GLSZM Radiomics PCa Ve Multiparametric magnetic resonance imaging (mpMRI) Quantitative parameters NPV OP-OSEM PPV kep mRMR T2w TURP WP SUVmean PSADTZ Positron emission tomography (PET) CG NGTDM Machine learning (ML) kNN ADC ratio GS CT PSMA mpMRI csPCa PI-RADS PSADCG PET
ISSN:	0730-725X, 1873-5894, 1873-5894
Online-Zugang:	Volltext
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