Uncovering Age-Specific Invasive and DCIS Breast Cancer Rules Using Inductive Logic Programming

Breast cancer is the most common type of cancer among women. Current clinical breast cancer diagnosis involves a biopsy, which is a costly, invasive and potentially painful procedure. Some researchers proposed models, based on mammography features and personal information, that help identify pre-biopsy invasive breast carcinoma and ductal carcinoma in situ (DCIS). Recently, a differential discriminating ability between invasive and DCIS has been linked to age. Based on this finding, we use an age-stratified mammography and biopsy relational dataset and apply Inductive Logic Programming (ILP) techniques to learn age-specific logical rules that classify invasive and DCIS occurrences. We then use statistical modeling to retrieve rules that have a significantly different performance across age-stratas. Thes