Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences

Abstract Objectives Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on m...

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Detalles Bibliográficos

Principais autores:	Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis
Formato:	Artigo
Idioma:	Inglês
Publicado:	SpringerOpen 2024-11-01
Series:	Insights into Imaging
Assuntos:	Radiomics Prostate cancer Machine learning MRI
Acceso en liña:	https://doi.org/10.1186/s13244-024-01783-9
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