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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Bibliografiske detaljer

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
Format:	Artigo
Sprog:	Inglês
Udgivet:	SpringerOpen 2024-11-01
Serier:	Insights into Imaging
Fag:	Radiomics Prostate cancer Machine learning MRI
Online adgang:	https://doi.org/10.1186/s13244-024-01783-9
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