Application of image analysis and neural networks to the pathology diagnosis of intraductal proliferative lesions of the breast

We studied whether a computer-assisted system using a combination of data collection by image analysis and analysis by neural networks can differentiate benign and malignant breast lesions. Forty-six intraductal lesions of the breast were studied by pathologists and by the computer-assisted system. Histological evaluation was performed independently by three pathologists, and the lesions were classified into pathologically malignant (n = 12), undetermined (n = 13), and benign (n = 21). Computerized nuclear image analysis was performed using the CAS200 (Cell Analysis Systems, Elmhurst, IL) system to obtain data on nuclear morphometric and textural features. A neural network was constructed using the morphometric and texture data obtained from teaching cases of malignant and benign lesions. Then data for unknown cases were classified by the constructed neural network into neural network-malignant (n = 11), -undetermined (n = 5), and -benign (n = 30). The agreement rate between the diagnosis by pathologists and judgement by the computer-assisted system was 75%, excluding pathologically undetermined lesions. There were four false-negative but no false-positive results. False-negative cases had nuclei that were quite different from those of the teaching cases. The agreement rate obtained using either morphometric data or texture data only was lower than that using a combination of both. Selection of appropriate teaching data and incorporation of both morphometric and textural parameters seemed important for obtaining more accurate results. The present data suggest that development of a computer-assisted histopathological diagnosis system for practical use may be possible.