We previously developed a computerized method to classify mammographic masses as benign or malignant. In this method, mammographic features that are similar to the ones used by radiologists are automatically extracted to characterize a mass lesion. These features are then merged by an artificial neural network (ANN), which yields an estimated likelihood of malignancy for each mass. The performance of the method was evaluated on an independent database consisting of 110 cases (60 benign and 50 malignant cases). The method achieved an Az of 0.91 from round-robin analysis in the task of differentiating between benign and malignant masses using the computer-extracted features only. As the most important clinical risk factor for breast cancer, age achieved a performance level (Az equals 0.79) similar to that (Az equals 0.77 and 0.80) of the computer-extracted spiculation features, which are the most important indicators for malignancy of a mass, in differentiating between the malignant and benign cases. In this study, age is included as an additional input feature to the ANN. The performance of the scheme (Az equals 0.93) is improved when age is included. However, the improvement is not found to be statistically significant. Our results indicated that age may be a strong feature in predicting malignancy of a mass. For this database, however, the inclusion of age may not have a strong impact on the determination of the likelihood for a mammographic mass lesion when the major mammographic characteristics (e.g., spiculation) of a mass are accurately extracted and analyzed along with other features using an artificial neural network.