## 1 Introduction  
Radiomic analysis is used to quantitatively analyze medical images by extracting a variety of informative features, also known as image biomarkers. This approach is the opposite of qualitative image interpretation, typically performed by experts [1, 2, 3, 4]. Since its introduction, radiomic analysis has been applied to diagnose pathologic lesions in various imaging modalities, such as computed tomography [5, 6] for saladenitis staging in submandibular gland and medication-related osteoporosis of the jaw, magnetic resonance imaging [7, 8] for [PERSON]'s tumor versus pleomorphic adenoma and osteomyelitis of the mandible, as well as ultrasonography (US) [5, 9, 10, 11, 12, 13, 14, 15, 16] for saladenitis staging in the submandibular gland and orbit, benign versus malignant breast nodules, triple-negative breast cancer, diabetic nephropathy, metastatic versus non-metastatic cervical lymph nodes, Sjogren's syndrome, and malignant parotid gland lesions. Studies on radiomic diagnoses involve selecting regions of interest (ROI), statistically determining significant radiomic features, and using regression analysis and/or machine learning for diagnosis [1, 2, 4, 5, 11, 12, 13, 14, 15, 16]. Additionally, the quantitative characteristic of radiomics has led to efforts for harmonizing radiomic features across different US units [17, 18].  
Compared to other imaging modalities, US offers the advantage of visualizing soft tissue without ionizing radiation or large units. In the oral and maxillofacial region, US is widely used to examine the salivary glands [5; 15; 16; 17; 19], muscles [20], cervical lymph nodes [14; 21], and small soft tissue areas [22]. When combined with doppler imaging, tumors or inflammation in salivary glands can be readily identified by observing the adjacent blood flow [23; 24].  
Recently, the application of radiomic approaches to analyze US has been widely researched. The influence of image preprocessing to radiomic features [9; 10] was studied, and it was found that intensity standardization with fixed bin size improves feature repeatability. Also, radiomic features were implemented in diagnosing pathologies of various organs [5; 10; 11; 12; 13; 14; 15; 16]. In terms of the salivary gland, radiomic diagnoses have been discussed for parenchymal disease, including sialadenitis and Sjogren syndrome [5; 15]. However, no studies have scrutinized radiomic features that represent normal radiomic variations due to differences in age and sex. Several studies have corroborated biological variations in salivary gland functions according to age and sex [25; 26]. Therefore, this study aimed to analyze radiomic features of the parotid glands with reduced shape effects in US images that contribute to the differentiation of age and sex to establish a normal reference for the radiomic diagnosis of parenchymal disease in the parotid gland.  
The following sections detail the acquisition and selection of the US images, the extraction and selection of radiomic features through the calculation of the intraclass correlation coefficient (ICC) between two different shapes of ROIs, the statistical algorithms used, and the performance of machine learning models in classifying age groups and sex. The selected radiomic features that yielded the best classification results are explained and discussed, as well as the characteristics of the classification algorithms and machine learning models. Finally, the contributions, novelty, and limitations of this study are scrutinized.