An interactive decision support system for breast fine needle aspiration cytology

To develop a computerized system to assist in the diagnosis of malignancy in breast fine needle aspiration cytology.

A Bayesian belief network was designed to control uncertainty and allow a diagnostic decision to be reached based on the sequential collection of cytologic information. Ten cytologic features were defined as clues that contribute to the diagnostic discrimination of benign and malignant aspirates. The impact of each feature on the diagnostic decision was quantified by a conditional probability matrix.

For the assessment of a new case, the computer guides the user through the diagnosis, prompting him or her for information on each of the diagnostic features in turn. For each feature, the user is presented with a series of digitally stored color microscopic images that have been selected to represent good examples of the different feature grades-e.g., pleomorphism: none, mild, moderate and severe. Each image is mapped to an overlapping curve, and by positioning a line on the spectrum where the user feels the case lies, a membership function vector is calculated and entered as evidence into the network. This results in an update in the belief in the diagnostic alternatives. After all the clues have been assessed, a final diagnostic probability is reported. In addition, a cumulative belief curve can be drawn that maps the change in the diagnostic probabilities after each piece of evidence has been submitted, providing unique insight into the diagnostic process.

Systems like this represent an important step forward in the use of descriptive classifiers. They impose consistency in terminology, improve reproducibility in the grading of cellular abnormalities and remove subjectivity in interpreting the significance of pvisual clues to diagnosis. As such, they represent a necessary tool in pathologic decision making.