Breast cancer diagnosis

This (Duke University Medical Center) report describes an artificial neural network (ANN) approach to computer-aided diagnosis of breast cancer from mammographic findings. An ANN has been developed to provide support for the clinical decision to perform breast biopsy. The system is designed to aid in the decision to biopsy those patients who have suspicious mammographic findings. The decision to biopsy can be viewed as a two stage process: (1) the mammographer views the mammogram and determines the presence or absence of image features, such as calcifications and masses; (2) the presence and description of these features, and the patient's medical history, are merged to form a diagnosis. The ANN system is an aid to the second step, and is motivated by the large fraction of biopsies that are benign.

While mammography is a sensitive procedure for detecting breast cancer, the positive predictive value (PPV) is low. Only 10% to 34% of women who undergo biopsy for mammographically-suspicious nonpalpable lesions, actually are found to have malignancy. Between 0.5% to 2.0% of all mammographic exams result in biopsy; several hundreds of thousands of biopsies are performed on benign lesions each year. The women undergoing biopsy for a benign finding are unnecessarily subjected to the discomfort, expense, potential complications, change in cosmetic appearance, and anxiety, that can accompany breast biopsy.

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