Houssam Nassif, David Page, Mehmet Ayvaci, Jude Shavlik & Elizabeth S. Burnside (2010).
Uncovering Age-Specific Invasive and DCIS Breast Cancer Rules Using Inductive Logic Programming. Proceedings of the 1st ACM International Health Informatics Symposium (IHI '10), Arlington, VA.

This publication is available in PDF.

Abstract:

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. These final rules reveal a number of interesting results. Although a palpable lump is more commonly associated with younger patients, it turns out to be a better predictor of invasive cancer in older women. A recurrence has a higher probability to be invasive in older and middle-aged women. A previously unreported rule revealed by our technique is that recurrence is more likely a DCIS predictor in younger women. This younger DCIS predicting rule effectively links the current diagnostic mammogram to older studies, and provides opposite predictions across the age divide. The resulting rules are age-specific, can help patients and their physicians make more informed decisions about managing their breast health, and constitute a personalized predictive model.

Return to the publications of the Univ. of Wisconsin Machine Learning Research Group.