Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.
MRI
glioblastoma
radiomics
robustness
segmentation
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Aug 2019
Aug 2019
Historique:
received:
16
01
2019
revised:
21
05
2019
accepted:
21
05
2019
pubmed:
28
5
2019
medline:
23
1
2020
entrez:
28
5
2019
Statut:
ppublish
Résumé
The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. Fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WI Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SD Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.
Identifiants
pubmed: 31131906
doi: 10.1002/mp.13624
pmc: PMC6692188
mid: NIHMS1032557
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3582-3591Subventions
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Informations de copyright
© 2019 American Association of Physicists in Medicine.
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