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Correlation of parotid gland tumor tissue types and maximum diameter with parotid gland surface segmentation: A
Enlai Shi1, Yumeng Huang2, Yi Fang1
1Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School / The First People's Hospital of Yancheng, Yancheng 224000, China.
Background:
Parotid gland tumors include different histological types, with varying clinical behaviors and surgical implications. A novel surface segmentation technique that relies on external anatomical landmarks offers a reliable three-region framework for pinpointing the tumor location.
Objective:
To assess the correlation of parotid gland tumor tissue types and the maximum tumor diameter with the surface segmentation. This retrospective study was conducted at the Yancheng First Hospital, China.
Materials And Methods:
Patients with parotid tumors who underwent preoperative Computed Tomography (CT) imaging and had confirmed postoperative pathological diagnoses were included. CT imaging was used to locate the tumors based on superficial-deep lobe division and three-zone surface segmentation and to measure the maximum tumor diameter. The postoperative histological types were recorded.
Results:
A total of 267 patients with a mean age of 55.9 ± 14.0 years and a male-to-female ratio of 1.1:1 were included. Age and sex distributions differed significantly according to the tumor type (p < 0.001). Tumor diameter was not associated with tissue type (p = 0.333). The histological distribution varied significantly across surface-segmented regions (p < 0.001). Warthin's tumors were most frequent in Region I, while pleomorphic adenomas were more common in combined Regions I/II/III. Tumor diameter also varied by region (p < 0.001), with larger and cross-regional tumors showing significantly greater mean diameters than single-region tumors (p < 0.001).
Significance:
Surface segmentation provides an anatomically and clinically useful framework for correlating tumor histology, size, and growth patterns. This approach provides an anatomically descriptive framework for understanding tumor distribution and growth patterns and may complement existing classification systems.
