Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions.
CT-based Quantification
Detectable Change
Lung Nodules
Morphology
Radiomics
Journal
Academic radiology
ISSN: 1878-4046
Titre abrégé: Acad Radiol
Pays: United States
ID NLM: 9440159
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
received:
28
01
2020
revised:
06
07
2020
accepted:
16
07
2020
pubmed:
24
8
2020
medline:
24
11
2021
entrez:
24
8
2020
Statut:
ppublish
Résumé
The 3-fold purpose of this study was to (1) develop a method to relate measured differences in radiomics features in different computed tomography (CT) scans to one another and to true feature differences; (2) quantify minimum detectable change in radiomics features based on measured radiomics features from pairs of synthesized CT images acquired under variable CT scan settings, and (3) ascertain and inform the recommendations of the Quantitative Imaging Biomarkers Alliance (QIBA) for nodule volumetry. Images of anthropomorphic lung nodule models were simulated using resolution and noise properties for 297 unique imaging conditions. Nineteen morphology features were calculated from both the segmentation masks derived from the imaged nodules and from ground truth nodules. Analysis was performed to calculate minimum detectable difference of radiomics features as a function of imaging protocols in comparison to QIBA guidelines. The minimum detectable differences ranged from 1% to 175% depending on the specific feature and set of imaging protocols. The results showed that QIBA protocol recommendations result in improved minimum detectable difference as compared to the range of possible protocols. The results showed that the minimum detectable differences may be improved from QIBA's current recommendation by further restricting the slice thickness requirement to be between 0.5 mm and 1 mm. Minimum detectable differences of radiomics features were quantified for lung nodules across a wide range of possible protocols. The results can be used prospectively to inform decision-making about imaging protocols to provide superior quantification of radiomics features.
Identifiants
pubmed: 32828664
pii: S1076-6332(20)30452-9
doi: 10.1016/j.acra.2020.07.029
pmc: PMC7895859
mid: NIHMS1623637
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1570-1581Subventions
Organisme : NIBIB NIH HHS
ID : P41 EB028744
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB001838
Pays : United States
Informations de copyright
Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
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