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

Ying-Hwey Nai,Dennis Lai Hong Cheong,Sharmili Roy, Trina Kok,Mary C Stephenson,Josh Schaefferkoetter,John J Totman,Maurizio Conti,Lars Eriksson,Edward G Robins,Ziting Wang,Wynne Yuru Chua,Bertrand Wei Leng Ang, Arvind Kumar Singha,Thomas Paulraj Thamboo,Edmund Chiong,Anthonin Reilhac

Magnetic resonance imaging（2023）

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摘要

ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

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关键词

Gleason score (GS),Machine learning (ML),Multiparametric magnetic resonance imaging (mpMRI),Positron emission tomography (PET),Quantitative parameters,Radiomics

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