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Turgay Ayer, Oguzhan Alagoz, Jagpreet Chhatwal, Jude W. Shavlik, Charles E. Kahn Jr and Elizabeth S. Burnside Breast cancer risk estimation with artificial neural networks revisited Cancer 116

Article first published online: 27 APR 2010 | DOI: 10.1002/cncr.25081

In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction based on discrimination performance, but none have assessed calibration, which is an equally important measure of accurate risk prediction. In this study, we show that an ANN trained on a large dataset of consecutive mammography findings can successfully discriminate malignant abnormalities from benign ones and accurately predict the probability of breast cancer for individual patients.

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