Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
| Autor: | Yuta Shibamoto, Satoshi Tsukahara, Norio Shiraki, Takatsune Kawaguchi, Daisuke Kawakita, Nobuhiro Hanai, Satoshi Osaga, Misugi Urano, Tsuneo Tamaki, Masaki Ogawa |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Elastic net regularization
Male Multivariate analysis Science Nose Neoplasms Esthesioneuroblastoma Olfactory Texture (geology) Sensitivity and Specificity Article 030218 nuclear medicine & medical imaging Diagnosis Differential 03 medical and health sciences 0302 clinical medicine Medical research Humans Basal cell Mathematics Aged Retrospective Studies Univariate analysis Multidisciplinary Receiver operating characteristic Olfactory Neuroblastoma business.industry Pattern recognition Middle Aged Neurology ROC Curve Multiple comparisons problem Carcinoma Squamous Cell Medicine Female Artificial intelligence Nasal Cavity business Tomography X-Ray Computed 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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