Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography.
Since the widespread adoption of mammographic screening in the 1980's there has been a significant increase in the detection and biopsy of both benign and malignant microcalcifications. Though current practice standards recommend that the positive predictive value (PPV) of breast biopsy should be in the range of 25-40%, there exists significant variability in practice. Microcalcifications, if malignant, can represent either a non-invasive or an invasive form of breast cancer. The distinction is critical because distinct surgical therapies are indicated. Unfortunately, this information is not always available at the time of surgery due to limited sampling at image-guided biopsy. For these reasons we conducted an experiment to determine whether a previously created Bayesian network for mammography could predict the significance of microcalcifications. In this experiment we aim to test whether the system is able to perform two related tasks in this domain: 1) to predict the likelihood that microcalcifications are malignant and 2) to predict the likelihood that a malignancy is invasive to help guide the choice of appropriate surgical therapy.
Burnside
Elizabeth S
ES
Department of Radiology, University of Wisconsin Medical School, 600 highland Avenue, Madison, WI 53792, USA. bburnside@mail.radiology.wisc.edu
Rubin
Daniel L
DL
Shachter
Ross D
RD
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000000981:diagnostic imaging*; D005103:Expert Systems; D006801:Humans; D008327:Mammography; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies