Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma.
Aged
Carcinoma, Squamous Cell
/ diagnostic imaging
Diagnosis, Differential
Esthesioneuroblastoma, Olfactory
/ diagnostic imaging
Female
Humans
Male
Middle Aged
Nasal Cavity
/ diagnostic imaging
Nose Neoplasms
/ diagnostic imaging
ROC Curve
Retrospective Studies
Sensitivity and Specificity
Tomography, X-Ray Computed
/ methods
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 02 2021
25 02 2021
Historique:
received:
05
10
2020
accepted:
11
02
2021
entrez:
26
2
2021
pubmed:
27
2
2021
medline:
21
12
2021
Statut:
epublish
Résumé
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists' interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists' visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists' interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
Identifiants
pubmed: 33633160
doi: 10.1038/s41598-021-84048-5
pii: 10.1038/s41598-021-84048-5
pmc: PMC7907098
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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