Computer-aided diagnosis of breast aspirates via expert systems

Two computer-driven expert systems trained to correctly diagnose 369 fine needle aspirates of the breast on the basis of nine cytologic descriptive parameters were tested on 70 newly obtained aspirates (57 benign and 13 malignant). The system generated by multisurface pattern separation misclassified one malignant test sample (i.e., one false negative) while the system generated by a connectionist algorithm (neural network) misclassified two benign test samples (i.e., two false positives). A decision tree misclassified three of the benign test samples (i.e., three false positives). These expert systems aid in the cytologic diagnosis of breast aspirates and can serve as models for other applications.