Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging

Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging

PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa) in prostate bi-parametric magnetic resonance imaging (MRI) using radiomics features. METHODS: MR...

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Bibliographic Details
Main Authors: Hakan Ayyıldız, Okan İnce, Esin Korkut, Merve Gülbiz Dağoğlu Kartal, Atadan Tunacı, Şükrü Mehmet Ertürk
Format: Article
Language:English
Published: Galenos Publishing House 2025-07-01
Series:Diagnostic and Interventional Radiology
Subjects:
prostate
magnetic resonance imaging
prostate cancer
radiomics
machine learning
bi-parametric magnetic resonance imaging
Online Access:https://www.dirjournal.org/articles/machine-learning-models-for-discriminating-clinically-significant-from-clinically-insignificant-prostate-cancer-using-bi-parametric-magnetic-resonance-imaging/doi/dir.2024.242856
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https://www.dirjournal.org/articles/machine-learning-models-for-discriminating-clinically-significant-from-clinically-insignificant-prostate-cancer-using-bi-parametric-magnetic-resonance-imaging/doi/dir.2024.242856

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