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.

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

“…ANN model is particularly helpful in differentiating the grey zone lesions where there is dilemma in the decision. ANN model was used in other problematic areas in cytology with remarkable success …”

Artificial neural network in breast lesions from fine‐needle aspiration cytology smear

“…ANNs have been applied occasionally in cytology such as in cervical cytology, effusion cytology, gastric, and thyroid cytology . However, ANNs have been rarely applied in breast cytology . In this study, we constructed an ANN model for differentiating malignant breast lesions from benign breast lesions on FNAC material.…”

“…10 However, ANNs have been rarely applied in breast cytology. [15][16][17][18] In this study, we constructed an ANN model for differentiating malignant breast lesions from benign breast lesions on FNAC material. Based on the cytological, image morpho-metric, densitometric, and GLCM data, we have successfully built the ANN model with great efficiency.…”

Performance of an artificial intelligence algorithm for reporting urine cytopathology

“…To our knowledge to date, significant work applying artificial intelligence (AI) to digital pathology has largely involved histopathology . Several groups have started applying similar promising techniques to computational cytology, including urine cytology …”