Comparison of robust to standardized CT radiomics models to predict overall survival for non-small cell lung cancer patients.
CT
lung cancer
multicentric
radiomics
robust
standardized
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
05
11
2019
revised:
28
02
2020
accepted:
24
04
2020
pubmed:
13
5
2020
medline:
15
5
2021
entrez:
13
5
2020
Statut:
ppublish
Résumé
Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection. Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (n In total, 113 stable features were identified (n Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.
Sections du résumé
BACKGROUND
BACKGROUND
Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection.
MATERIALS AND METHODS
METHODS
Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (n
RESULTS
RESULTS
In total, 113 stable features were identified (n
CONCLUSION
CONCLUSIONS
Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.
Types de publication
Journal Article
Multicenter Study
Randomized Controlled Trial
Langues
eng
Sous-ensembles de citation
IM
Pagination
4045-4053Subventions
Organisme : Swiss National Science Foundation
ID : 310030_173303
Pays : Switzerland
Informations de copyright
© 2020 American Association of Physicists in Medicine.
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