T. Ayer, O. Alagoz, J. Chhatwal, J. Shavlik, C. Kahn & E. Burnside (2010).
Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration. Cancer. doi: http://dx.doi.org/10.1002/cncr.25081

This publication is

Abstract:

Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction. All studies have reported discrimination performance, but not one has assessed calibration, which is an equivalently important measure for accurate risk prediction. In this study, the authors have evaluated whether an artificial neural network (ANN) trained on a large prospectively collected dataset of consecutive mammography findings can discriminate between benign and malignant disease and accurately predict the probability of breast cancer for individual patients.Our dataset consisted of 62, 219 consecutively collected mammography findings matched with the Wisconsin State Cancer Reporting System. The authors built a 3-layer feedforward ANN with 1000 hidden-layer nodes. The authors trained and tested their ANN by using 10-fold cross-validation to predict the risk of breast cancer. The authors used area the under the receiver-operating characteristic curve (AUC), sensitivity, and specificity to evaluate discriminative performance of the radiologists and their ANN. The authors assessed the accuracy of risk prediction (ie, calibration) of their ANN by using the Hosmer-Lemeshow (H-L) goodness-of-fit test.Their ANN demonstrated superior discrimination (AUC, 0.965) compared with the radiologists (AUC, 0.939; P < .001). The authors' ANN was also well calibrated as shown by an H-L goodness of fit P-value of .13.The authors' ANN can effectively discriminate malignant abnormalities from benign ones and accurately predict the risk of breast cancer for individual abnormalities.

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