Artificial neural network in diagnosis of lobular carcinoma of breast in fine‐needle aspiration cytology

Abstract

In this study, we applied artificial neural network (ANN) for the diagnosis of lobular carcinoma in fine-needle aspiration cytology (FNAC) material. We selected a total of 64 cases of histology proven breast lesions consisting of 20 fibroadenomas, 28 infiltrating ductal carcinomas (IDC), and 16 infiltrating lobular carcinomas (ILC). Detailed cytomorphological features were studied on representative Haematoxylin-Eosin (H&E) and May-Grunwald Giemsa stained slides. Image morphometric analysis was performed on Haematoxylin-Eosin stained smears to study nuclear area, diameter, perimeter, roundness, convex area, and convex perimeter. Both the qualitative cytological features and objective morphometric data were collected and a total of 18 variables were studied. Back propagation ANN was designed and this data were used as input values. ANN network was designed as 34-17-3. There were a total of 34 first layers neurons, 17 hidden neurons and three output neurons. The total cases were randomly divided automatically by the program into three groups: training set (40), validation set (8), and test set (16). After the successful training, the program was able to differentiate all the benign and lobular carcinoma cases and majority of the ductal carcinoma cases. In test set, the ANN program successfully classified all the cases of benign, and ILC cases and six of seven IDC cases. A suitably designed ANN may be able to diagnose the lobular carcinoma of breast on FNAC material. ANN is an efficient software program with immense potential.