RESUMO
Objective: This study explored the value of different radiomic models based on multiphase computed tomography in differentiating parotid pleomorphic adenoma (PA) and basal cell tumor (BCA) concerning the predominant phase and the optimal radiomic model. Methods: This study enrolled 173 patients with pathologically confirmed parotid tumors (training cohort: n=121; testing cohort: n=52). Radiomic features were extracted from the nonenhanced, arterial, venous, and delayed phases CT images. After dimensionality reduction and screening, logistic regression (LR), K-nearest neighbor (KNN) and support vector machine (SVM) were applied to develop radiomic models. The optimal radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was performed to analyze clinical-radiological characteristics and to identify variables for developing a clinical model. A combined model was constructed by integrating clinical and radiomic features. Model performances were assessed by ROC curve analysis. Results: A total of 1036 radiomic features were extracted from each phase of CT images. Sixteen radiomic features were considered valuable by dimensionality reduction and screening. Among radiomic models, the SVM model of the arterial and delayed phases showed superior predictive efficiency and robustness (AUC, training cohort: 0.822, 0.838; testing cohort: 0.752, 0.751). The discriminatory capability of the combined model was the best (AUC, training cohort: 0.885; testing cohort: 0.834). Conclusions: The diagnostic performance of the arterial and delayed phases contributed more than other phases. However, the combined model demonstrated excellent ability to distinguish BCA from PA, which may provide a non-invasive and efficient method for clinical decision-making.
RESUMO
BACKGROUND: Nutrition and cell size play an important role in the determination of caste differentiation in queen and worker of honeybees (Apis mellifera), whereas the haploid genome dominates the differentiation of drones. However, the effects of female developmental environment on the development of males remain unclear. In this study, young drone larvae were transferred into worker cells (WCs) or remained in drone cells (DCs) to rear drones. The drone larvae were also grafted into queen cells (QCs) for 48 h and then transplanted into drone cells until emerging. Morphological indexes and reproductive organs of these three types of newly emerged drones were measured. Newly emerged drones and third instar drone larvae from WCs, DCs and QCs were sequenced by RNA sequencing (RNA-Seq). RESULTS: The amount of food remaining in cells of the QC and WC groups was significantly different to that in the DC group at the early larval stage. Morphological results showed that newly emerged DC drones had bigger body sizes and more well-developed reproductive tissues than WC and QC drones, whereas the reproductive tissues of QC drones were larger than those of WC drones. Additionally, whole body gene expression results showed a clear difference among three groups. At larval stage there were 889, 1761 and 1927 significantly differentially expressed genes (DEGs) in WC/DC, QC/DC and WC/QC comparisons, respectively. The number of DEGs decreased in adult drones of these three comparisons [678 (WC/DC), 338 (QC/DC) and 518 (WC/QC)]. A high number of DEGs were involved in sex differentiation, growth, olfaction, vision, mammalian target of rapamycin (mTOR), Wnt signaling pathways, and other processes. CONCLUSIONS: This study demonstrated that the developmental environment of honeybee females can delay male development, which may serve as a model for understanding the regulation of sex differentiation and male development in social insects by environmental factors.
