Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 12 2020
04 12 2020
Historique:
received:
19
06
2020
accepted:
23
11
2020
entrez:
5
12
2020
pubmed:
6
12
2020
medline:
30
3
2021
Statut:
epublish
Résumé
This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10
Identifiants
pubmed: 33277570
doi: 10.1038/s41598-020-78338-7
pii: 10.1038/s41598-020-78338-7
pmc: PMC7718925
doi:
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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