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

• Published in: Magnetic resonance imaging Vol. 100; pp. 64 - 72
• Main Authors: 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
• Language: English
• Published: Netherlands Elsevier Inc 01.07.2023
• Subjects: 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

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...