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Survival Outcomes and Patterns of Failure in Maxillary Alveolus Squamous Cell Carcinoma.

Muhammad Umar QayyumAhmed Ali KeerioMuhammad FaisalAsma RashidRaza HussainArif Jamshed
Published in: International archives of otorhinolaryngology (2023)
Introduction  Squamous cell carcinoma (SCC) of the maxillary alveolus is a relatively rare disease. There is lack of data on this subsite as compared with other sites. The factors that affect survival in cases of maxillary alveolar SCC are tumor stage, local and cervical metastases, histological grading, and the margin status. Objectives  To evaluate the overall survival (OS), the disease free survival (DFS), and the complex interaction and effects of margin status, histological differentiation, habits (such as smoking and the use of smokeless tobacco products), and cervical and distant metastases based on clinicopathological data. Methods  We examined the electronic database at our hospital from 2003 to 2017. We included all cases with a histopathological diagnosis of SCC of the maxillary alveolus. Tumors originating primarily from the maxillary alveolus were included, while those originating from adjacent subsites, like the hard palate, the buccal mucosa or the maxillary sinus were excluded. We also excluded all the patients who were not operated on with a curative intent. Results  More than half of the patients had stage-IV tumors at the time of presentation, while only one fourth of them had nodal metastasis. The rate of recurrence increased in cases of primary tumors in advanced stages and the degree of histological differentiation. The 2-year and 5-year OS rates were of 54.5% (18 patients) and 30.3% (10 patients) respectively. Conclusion  Primary tumors in advanced stages, histological grade, and presence of nodal metastasis are poor prognostic markers in terms of long-term survival.
Keyphrases
  • squamous cell carcinoma
  • end stage renal disease
  • free survival
  • ejection fraction
  • chronic kidney disease
  • prognostic factors
  • emergency department
  • artificial intelligence
  • deep learning
  • adverse drug