Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images.
Malignancy grade
Parotid gland cancer
Radiomic features
Topology
Journal
Magma (New York, N.Y.)
ISSN: 1352-8661
Titre abrégé: MAGMA
Pays: Germany
ID NLM: 9310752
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
27
08
2022
accepted:
22
03
2023
revised:
12
03
2023
medline:
18
9
2023
pubmed:
20
4
2023
entrez:
20
04
2023
Statut:
ppublish
Résumé
The malignancy grades of parotid gland cancer (PGC) have been assessed for a decision of treatment policies. Therefore, we have investigated the feasibility of topology-based radiomic features for the prediction of parotid gland cancer (PGC) malignancy grade in magnetic resonance (MR) images. Two-dimensional T1- and T2-weighted MR images of 39 patients with PGC were selected for this study. Imaging properties of PGC can be quantified using the topology, which could be useful for assessing the number of the k-dimensional holes or heterogeneity in PGC regions using invariants of the Betti numbers. Radiomic signatures were constructed from 41,472 features obtained after a harmonization using an elastic net model. PGC patients were stratified using a logistic classification into low/intermediate- and high-grade malignancy groups. The training data were increased by four times to avoid the overfitting problem using a synthetic minority oversampling technique. The proposed approach was assessed using a 4-fold cross-validation test. The highest accuracy of the proposed approach was 0.975 for the validation cases, whereas that of the conventional approach was 0.694. This study indicated that topology-based radiomic features could be feasible for the noninvasive prediction of the malignancy grade of PGCs.
Identifiants
pubmed: 37079154
doi: 10.1007/s10334-023-01084-0
pii: 10.1007/s10334-023-01084-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
767-777Subventions
Organisme : Japan Society for the Promotion of Science
ID : 17K15808
Informations de copyright
© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).
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