Computer-assisted image interpretation: use of a neural network to differentiate tubular carcinoma from sclerosing adenosis.

Measurement of nuclear and glandular size and shape features was carried out on 18 cases of sclerosing adenosis and 18 cases of tubular carcinoma. Modified Bonferroni analysis showed that glandular surface density and the coefficient of variation of luminal form factor were significant in discriminating between these two lesions. These two histologic features, together with the diagnosis, were used to train a neural network implementing a backpropagation algorithm. Following training, the network correctly classified 33 of the 36 cases in the training set (92%). Furthermore, the network correctly classified 19 of 19 cases in a test set consisting of cases that were not used to train the network. These results suggest that neural networks may be useful to assist in the differential diagnosis of histologically similar lesions.