BREAST CANCER RISK EVALUATION - A CORRELATION BETWEEN MAMMOGRAPHIC DENSITY AND THE GAIL MODEL

The Gail model is a statistical tool, which assesses breast cancer probability, based on nonmodifiable risk factors.  In contrast, the evaluation of mammographic breast density is an independent and dynamic risk factor influenced by interventions modifying breast cancer risk incidence.

The aim of the present study is to compare the possibilities for risk factor integration and analysis and to search for a correlation between mammographic density and the Gail model for breast cancer risk evaluation. The subject of this prospective study is a cohort of 107 women at ages from 37 to 71 years, who have had benign breast diseases, digital mammograms, and Gail model risk evaluation.
Mammographic density is evaluated in craniocaudal projection subjectively visually and objectively using the computer imaging software. (Image J software) The Gail risk evaluation is completed using the standardized NCI questionnaire (Breast Cancer Risk Assessment Tool).
In concordance with the Breast Imaging Reporting and Data System (BI-RAD) by ACR, mammographic density is evaluated using a four-grade scale. Low density D1 (less than 25%) was determined in 24 cases, D2 (25-50%) in 36 cases, D3 (51-75%) in 31 cases and high density D4 (greater than 75%) in 16 cases.
According to the Gail model, 80 (74,8%) of the examined patients did not have an increased risk (less than 1,67% for a five-year period), whereas the remaining 27 (25,2%) had a statistically significant increase in risk (greater than 1,67% for a five-year period). Women with increased risk more often present with denser breast (34% with D3, D4 versus 18,3% for D1, D2).
The Gail model does not adequately explain the correlation between breast density and statistically calculated risk. The development of more detailed tools, which take into consideration breast density, as well as other risk factors, may be helpful for a more accurate evaluation of the individual risk for breast cancer.

Editor-in-Chief: Dr. Ivan Inkov  

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