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

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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Bibliographic Details
Main Authors: 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: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Insights into Imaging
Subjects:
Radiomics
Prostate cancer
Machine learning
MRI
Online Access:https://doi.org/10.1186/s13244-024-01783-9
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