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A retrospective study of nonneoplastic and neoplastic disorders of the salivary glands.

Sorin VamesuOana Andreea UrsicaAna Maria GuritaRaluca Ioana VodaMariana DeacuMariana AschieMadalina BosoteanuGeorgeta Camelia CozaruAnca Florentina MitroiCristian Ionuț Orasanu
Published in: Medicine (2023)
The spectrum of major and minor salivary gland disorders varies widely. Epidemiological data on some injury categories are rare and often not up-to-date. This study aims to analyze epidemiological data using clinical, paraclinical, and histopathological parameters. Study was carried out for 5 years on the nonneoplastic and tumoral pathology of the salivary glands. Data were statistically analyzed using the appropriate parameters. Data analysis according to the biological behavior of the lesions revealed great heterogeneity. Statistically significant correlations were observed between the type of injury, age (P = .002) and gender (0.033). The environment of origin of the patients as well as the comorbidities reflected in most cases the nature of the process. Associations were also observed between the biological behavior of the lesions and the hemicranial topography (P = .019), the type of salivary gland (P = .024), and the surgical technique used (P < .001). Most cases were identified in the major salivary glands, often in the parotid. The most common diseases are represented by nonspecific chronic sialadenitis (nonneoplastic lesion), pleomorphic adenoma and Warthin tumor (benign tumors), mucoepidermoid carcinoma (malignant tumor), and squamous carcinoma (secondary tumor). They presented axial diameters between 2 to 95 mm. The most used curative technique was subtotal excision with facial nerve preservation. In conclusion, the study highlighted the main epidemiological aspects of salivary gland disorders. Some data agree with the specialty literature, and particular aspects are also observed. Therefore, this research is useful both in the medical and research fields.
Keyphrases
  • data analysis
  • electronic health record
  • big data
  • systematic review
  • prognostic factors
  • healthcare
  • end stage renal disease
  • newly diagnosed
  • single cell
  • machine learning
  • high grade
  • deep learning