Comparative Analysis between the Gail, Tyrer-Cuzick and BRCAPRO Models for Breast Cancer Screening in Brazilian Population.
OBJECTIVE
To analyze the diagnostic accuracy of predictive models of breast cancer risk for the Brazilian population.


METHOD
A cross-sectional, study was conducted in a sample of 382 women aged 35-69 years who were users of the Unified Health System (SUS) residing in a municipality in southern Brazil.


RESULTS
The results showed that the Tyrer-Cuzick model had the highest mean risk values and estimates (proportion) for predicting the 5-year risk of breast cancer, reaching a maximum risk of ±1.63% in the 60-64 year age group. For the 90-year risk, a maximum risk of ±12.8% was predicted for the 50-54 year age group using this model. The 5-year risk calculated by the three tools increased progressively with increasing age, where the mean risk was ±0.8% in women aged 35-39 and reached ±1.50% in women aged 65-69. The 90-year risk declined with increasing age only in the Tyrer-Cuzick model, from ±10.8% to ±9%. The BRCAPRO model presented a greater sensitivity compared to the Gail and Tyrer-Cuzick models. And, the model that presented greater specificity was Gail.


CONCLUSION
The Tyrer-Cuzick model presented the highest risk estimates for 5 years and 90 years in the studied population, however, this data is not enough to validate this tool, since when analyzing the sensitivity and specificity the BRCAPRO and Gail have the highest values respectively.
['Stevanato|Kely Paviani|KP|0000-0003-1872-8246', 'Pedroso|Raíssa Bocchi|RB|', 'Iora|Pedro|P|', 'Santos|Lander Dos|LD|', 'Pelloso|Fernando Castilho|FC|', 'Melo|Willian Augusto de|WA|', 'Carvalho|Maria Dalva de Barros|MDB|', 'Pelloso|Sandra Marisa|SM|']
D000328:Adult D000368:Aged D001938:Brazil D001940:Breast D001943:Breast Neoplasms D003430:Cross-Sectional Studies D055088:Early Detection of Cancer D005260:Female D006801:Humans D008875:Middle Aged D008962:Models, Theoretical D018570:Risk Assessment D012307:Risk Factors D012680:Sensitivity and Specificity
breast cancer Screening Risk models