Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images.
FDG PET
head and neck cancer
multi-site performance analysis
radiomics
segmentation
Journal
Tomography (Ann Arbor, Mich.)
ISSN: 2379-139X
Titre abrégé: Tomography
Pays: Switzerland
ID NLM: 101671170
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
entrez:
18
6
2020
pubmed:
18
6
2020
medline:
25
6
2021
Statut:
ppublish
Résumé
Quantitative imaging biomarkers (QIBs) provide medical image-derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.
Identifiants
pubmed: 32548282
doi: 10.18383/j.tom.2020.00004
pii: TOMO.2020.00004
pmc: PMC7289247
doi:
Substances chimiques
Biomarkers, Tumor
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
65-76Subventions
Organisme : NCI NIH HHS
ID : U24 CA180918
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002537
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA140206
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA086862
Pays : United States
Organisme : NCI NIH HHS
ID : R50 CA211270
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA148131
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB004640
Pays : United States
Informations de copyright
© 2020 The Authors. Published by Grapho Publications, LLC.
Déclaration de conflit d'intérêts
Conflict of Interest: The authors have no conflict of interest to declare.
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