Differences between Occidental and Oriental postmenopausal women in cutoff level of endometrial thickness for endometrial cancer screening by vaginal scan

1995 ◽  
Vol 172 (5) ◽  
pp. 1494-1495 ◽  
Author(s):  
Hiroshi Tsuda ◽  
Masami Kawabata ◽  
Kazume Kawabata ◽  
Kumio Yamamoto ◽  
Atsuo Hidaka ◽  
...  
2020 ◽  
Vol 3 (9) ◽  
pp. e2018327
Author(s):  
Jacopo Troisi ◽  
Antonio Raffone ◽  
Antonio Travaglino ◽  
Gaetano Belli ◽  
Carmen Belli ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Luca Giannella ◽  
Kabala Mfuta ◽  
Tiziano Setti ◽  
Lillo Bruno Cerami ◽  
Ezio Bergamini ◽  
...  

Objective. To develop and test a risk-scoring model for the prediction of endometrial cancer among symptomatic postmenopausal women at risk of intrauterine malignancy.Methods. We prospectively studied 624 postmenopausal women with vaginal bleeding and endometrial thickness > 4 mm undergoing diagnostic hysteroscopy. Patient characteristics and endometrial assessment of women with or without endometrial cancer were compared. Then, a risk-scoring model, including the best predictors of endometrial cancer, was tested. Univariate, multivariate, and ROC curve analysis were performed. Finally, a split-sampling internal validation was also performed.Results. The best predictors of endometrial cancer were recurrent vaginal bleeding (odds ratio(OR)=2.96), the presence of hypertension(OR=2.01)endometrial thickness > 8 mm(OR=1.31), and age > 65 years(OR=1.11). These variables were used to create a risk-scoring model (RHEA risk-model) for the prediction of intrauterine malignancy, with an area under the curve of 0.878 (95% CI 0.842 to 0.908;P<0.0001). At the best cut-off value (score ≥ 4), sensitivity and specificity were 87.5% and 80.1%, respectively.Conclusion. Among symptomatic postmenopausal women with endometrial thickness > 4 mm, a risk-scoring model including patient characteristics and endometrial thickness showed a moderate diagnostic accuracy in discriminating women with or without endometrial cancer. Based on this model, a decision algorithm was developed for the management of such a population.


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