Prediction of Predisposing Factors to Breast Tumours Using Artificial Neural Network

Abstract

Breast tumour occurrences are increasing among women worldwide. The predisposing factors remain unclear because of their heterogeneity, hence the need for computer-aided approach in prediction. This study investigated the occurrence of breast tumours; connective tissue changes; estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), cytokeratin 7 (CK7), and Kiel-67 (Ki-67) expressions; and used artificial neural network (ANN) to predict the important predisposing factors. Data from ninety-six women aged 13-78 years and fifteen breast biopsies from the Histopathology Laboratory, University of Calabar Teaching Hospital were obtained. The formalin-fixed-paraffin-wax-embedded tissues were stained with hematoxylin and eosin (H&E), van Gieson, aqueous beetroot, and Colloidal iron-PAS. Ten malignant tissue blocks were immunohistochemically-stained for ER, PR, HER2, CK7, and Ki-67. Data were analyzed with Chi-square and ANN of Statistical Package of Social Sciences (SPSS) software. In the results, breast cancers were 35(36.5%) of the 96 womenand age was a predisposing factor (p=0.001). Beetroot demonstrated stroma hyalinization. The malignant tumours were mostly HER2+ 8(80%). Chi-square showed HER2+, CK7+, and Ki-67+-tissues had collagen and mucin depositions (p=0.001). Using ANN, mucin deposition, stroma hyalinization, CK7, HER2+, and age were the most important predisposing factors (p=0.006). Breast cancer is characterized mostly by acid mucin deposition and stroma hyalinization