Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer.
machine learning
non-small cell lung cancer
prognosis
prospective study
radiomics
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
09 Apr 2021
09 Apr 2021
Historique:
received:
21
01
2021
revised:
28
02
2021
accepted:
02
03
2021
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
1
5
2021
Statut:
epublish
Résumé
Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular; however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields.
Identifiants
pubmed: 33918681
pii: diagnostics11040675
doi: 10.3390/diagnostics11040675
pmc: PMC8069690
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Ministère de l'Enseignement Supérieur et de la Recherche
ID : R16063NN
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