MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.

Ignacio Dominguez,Odette Rios-Ibacache,Paola Caprile, Jose Gonzalez,Ignacio F San Francisco,Cecilia Besa

Diagnostics (Basel, Switzerland)（2023）

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摘要

Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.

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关键词

prostate cancer,Gleason score,texture analysis,bpMRI,machine learning,radiomics

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