Deep Lobe Parotid Tumors: A Systematic Review and Meta-analysis.

Autor: Aasen, Margaret H., Hutz, Michael J., Yuhan, Brian T., Britt, Christopher J.
Zdroj: Otolaryngology-Head & Neck Surgery; Jan2022, Vol. 166 Issue 1, p60-67, 8p
Abstrakt: Objective: We performed a systematic review and meta-analysis of deep lobe parotid tumors to evaluate their unique characteristics.Data Sources: PubMed/Medline, Embase, Web of Sciences, and Cochrane Library databases were queried for relevant literature.Review Methods: Studies were individually assessed by 2 independent reviewers. Risk of bias was assessed with the Cochrane bias tool, GRADE criteria, and MINORS criteria. Results were reported according to the PRISMA guidelines. Statistical analysis was performed by comparing rates of malignancy between deep and superficial lobe tumors.Results: In total, 8 studies including 379 deep lobe parotid tumors met inclusion criteria. Mean age at diagnosis was 44.9 years. Computed tomography scan was the most common imaging modality. Preoperative diagnostic fine-needle aspiration was utilized in 39.4% of patients and demonstrated high sensitivity for malignant disease. The most common approach was subtotal parotidectomy with facial nerve preservation (58.9%). The rate of malignancy was 26.6%, which was significantly higher than that of the superficial lobe tumors in this study (risk ratio, 1.25; 95% CI, 1.01-1.56). The rate of temporary postoperative facial nerve weakness between deep and superficial lobe tumors was 32.5% and 11.7%, respectively.Conclusion: Deep lobe parotid tumors had a 26.6% rate of malignancy. On meta-analysis, deep lobe tumors appeared to have higher rates of malignancy than superficial lobe tumors. Surgical excision of deep lobe tumors showed increased rates of temporary facial nerve paresis as compared with superficial lobe tumors. Computed tomography scan was the most common imaging modality. There were limited data regarding the utility of fine-needle aspiration. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index