کلیدواژهها
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Breast neoplasms, Case-control studies, Risk assessment, Software validation, Statistical models
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چکیده
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The validity of the IBIS model was evaluated for predicting breast cancer risk in the Iranian population. We
performed a case-control study, enrolling 1633 Iranian women. We indicated that the discrimination of cases
and noncases based on the IBIS model was better than the BOADICEA model for the Iranian population,
although the discrimination of both models was relatively low.
Background: Several approaches have been suggested for incorporating risk factors to predict the future risk of
breast cancer. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA)
and International Breast Cancer Intervention Study (IBIS) are among these approaches. We compared the
performance of these models in discriminating between cases and noncases in the Iranian population. Patients and
Methods: We performed a case-control study in Tehran, from November 2015 to April 2016, and enrolled 1633
women aged 24 to 75 years, including 506 cases of breast cancer, 916 population-based controls, and 211 clinicbased
controls. We calculated and compared the risk of breast cancer predicted by the IBIS and BOADICEA
models and the logistic regression model. For model discrimination, we computed the area under the receiver
operating characteristic (ROC) curve. Results: The risk of breast cancer predicted by the IBIS model was higher than
the BOADICEA model, but lower than the logistic model. The area under the ROC plots indicated that the logistic
regression model showed better discrimination between cases and noncases (71.53%) compared with the IBIS model
(49.36%) and BOADICEA model (35.84%). Based on the Pierson correlation coefficient, the correlation between IBIS
and BOADICEA models was much stronger than the correlation between IBIS and logistic models (0.3884 and 0.1639,
respectively). Conclusion: The IBIS model discriminated cases and noncases better than the BOADICEA model in the
Iranian population. However, the discrimination of the logistic regression
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