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Soft palatal melanosis, a simple predictor for neoplasia in the upper aerodigestive tract in Japanese alcoholic men.

Kenro HirataAkira YokoyamaRieko NakamuraTai OmoriHirofumi KawakuboTakeshi MizukamiKatsuya MaruyamaTakanori KanaiTetsuji Yokoyama
Published in: Cancer science (2017)
Soft palatal melanosis can be detected by visual inspection during routine physical examination or even personally in a mirror. The aim of this study was to evaluate the association between squamous cell neoplasia in the upper aerodigestive tract (UAT) and soft palatal melanosis. We reviewed digitized records of high-quality endoscopic images of the soft palate of 1786 Japanese alcoholic men who underwent endoscopic screening. Soft palatal melanosis was observed in 381 (21.3%) of the subjects (distinct, 6.3%). Older age, an inactive heterozygous aldehyde dehydrogenase-2 genotype, smoking, and a high mean corpuscular volume were positively associated with the presence of soft palatal melanosis. The age-adjusted odds ratio (95% confidence interval) for UAT neoplasia was 1.92 (1.40-2.64) in the group with melanosis and 2.51 (1.55-4.06) in the group with distinct melanosis, compared with the melanosis-free group. A multivariate analysis showed that the presence of soft palatal melanosis was independently associated with a high risk of UAT neoplasia. We calculated the individual number of risk factors out of four easily identifiable and significant factors: age ≥55 years, current/former alcohol flushing, mean corpuscular volume ≥106 fL, and distinct soft palatal melanosis. Compared with the risk-factor-free condition, the odds ratio (95% confidence interval) values of UAT neoplasia for one, two, three, and four risk factors were 1.49 (0.97-2.30), 3.14 (2.02-4.88), 4.80 (2.71-8.51), and 7.80 (2.17-28.1), respectively. The presence of soft palatal melanosis provides a simple new strategy for identifying heavy drinkers with a high risk for UAT neoplasia.
Keyphrases
  • risk factors
  • high grade
  • physical activity
  • mental health
  • ultrasound guided
  • machine learning
  • optical coherence tomography