F. Kuusisto, I. Dutra, H. Nassif, Y. Wu, M. Klein, H. Neuman, J. Shavlik & E. Burnside (2013).
Using Machine Learning to Identify Benign Cases with Non-Definitive Biopsy. Proceedings of IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom), Lisbon, Portugal.

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Abstract:

When mammography reveals a suspicious finding, a core needle biopsy is usually recommended. In 5% to 15% of these cases, the biopsy diagnosis is non-definitive and a more invasive surgical excisional biopsy is recommended to confirm a diagnosis. The majority of these cases will ultimately be proven benign. The use of excisional biopsy for diagnosis negatively impacts patient quality of life and increases costs to the healthcare system. In this work, we employ a multi-relational machine learning approach to predict when a patient with a non-definitive core needle biopsy diagnosis need not undergo an excisional biopsy procedure because the risk of malignancy is low.

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