Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma

Abstract

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.

A Scoping Review of Artificial Intelligence Research in Rhinology

“…Of the 79 articles identified, 3 were published in 2017, 17–19 5 were published in 2018, 20–24 9 were published in 2019, 25–33 15 were published in 2020, 34–48 31 were published in 2021, 49–79 and 16 were published in 2022 80…”

“…Articles were published in journals of otolaryngology (n = 19), 17,18,23,26,28,30,36,38,39,42,46,52,57,65,72,77,86,87,94 radiology (n = 21), 19,22,25,29,31,33,34,45,48,51,54,55,58,67,73,74,81,83–85,95 medical sciences (n = 19), 32,41,44,47,53,61,63,66,68–70,76,78–80,89–91,93 or other areas of medicine (n = 20). 20,21,24,27,35,37,…”

“…Radiology Diagnostics There were 42 articles that used an application of AI for radiology/imaging diagnostics in rhinology. 19,21,22,25,26,29,31,38–43,45,46,48–51,54,55,58,59,62–64,67,69,75,78,80–86,89,91–93,95 Imaging tools used include CT (n = 25), 21,22,25,26,38–43,45,46,55,58,59,63,69,75,81–83,89,91,92,…”

“…The remaining 27 articles aimed to identify or differentiate between various pathologies 19,26,29,31,39,40,42,46,48,49,54,55,58,62–64,67,69,75,80–86,89 including concha bullosa (n = 1), 42 CRS (n = 3), 39,46,83 fungal ball (n = 1), 82 inverted papilloma (n = 3), 19,69,86 nasal cancers (n = 4), 19,49,63,86 maxillary defects (n = 1), 75 maxillary sinus lesions (n = 3), 55,58,81 maxillary sinusitis (n = 6), 29,31,54,62,64,85 nasal fractures (n = 1), 89 nasopharyngeal cancers (n = 4) 40,48,67,80 or other pathologies (n = 1), 84 and osteomeatal complex inflammation (n = 1). 26 Diagnostic accuracy was rated as excellent (n = 12), 39,40,42,46…”

Use of Computed Tomography–Based Texture Analysis to Differentiate Benign From Malignant Salivary Gland Lesions

“…Therefore, this is considered to be a more objective technique than traditional methods. Computed tomography texture analysis has been widely available in a variety of tumors and nononcologic applications, including head and neck cancer, 8 liver cancer, 9 lung cancer, 10 quantification of hepatic fibrosis, 11 and pulmonary fibrosis, 12 in predicting pathological features and assessment of therapy and prognosis. In addition, contrast-enhanced CT is also used in CT texture analysis because of various blood supply characteristics of the tumor 13–15 .…”