Accurate Tumor Delineation
machine learning
non-small cell lung cancer
prognosis
radiomics
segmentation
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2021
2021
Historique:
received:
17
06
2021
accepted:
20
09
2021
entrez:
4
11
2021
pubmed:
5
11
2021
medline:
5
11
2021
Statut:
epublish
Résumé
The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose ( A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively ( Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.
Sections du résumé
BACKGROUND
BACKGROUND
The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (
METHODS
METHODS
A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively (
RESULTS
RESULTS
Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89
CONCLUSION
CONCLUSIONS
Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.
Identifiants
pubmed: 34733779
doi: 10.3389/fonc.2021.726865
pmc: PMC8560021
doi:
Types de publication
Journal Article
Langues
eng
Pagination
726865Informations de copyright
Copyright © 2021 Sepehri, Tankyevych, Iantsen, Visvikis, Hatt and Cheze Le Rest.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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