MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features
Published in Mdpi Diagnostics, 2022
This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information.
Recommended citation: Dominguez, I., Rios-Ibacache, O., Caprile, P., Gonzalez, J., San Francisco, I. F., & Besa, C. (2023). MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features. Diagnostics, 13(17), 2779. https://doi.org/10.3390/diagnostics13172779
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