Validation of a constraint satisfaction neural network for breast cancer diagnosis: New results from 1,030 cases

Previously, we presented a Constraint Satisfaction Neural Network (CSNN) to predict the outcome of breast biopsy using mammographic and clinical findings. Based on 500 cases, the study showed that CSNN was able to operate not only as a predictive but also as a knowledge discovery tool. The purpose of this study is to validate the CSNN on a database of additional 1,030 cases. An auto-associative backpropagation scheme was used to determine the CSNN constraints based on the initial 500 patients. Subsequently, the CSNN was applied to 1,030 new patients (358 patients with malignant and 672 with benign lesions) to predict breast lesion malignancy. For every test case, the CSNN reconstructed the diagnosis node given the network constraints and the external inputs to the network. The activation level achieved by the diagnosis node was used as the decision variable for ROC analysis. Overall, the CSNN continued to perform well over this large dataset with ROC area of Az = 0.81 ± 0.02. However, the diagnostic performance of the network was inferior in cases with missing clinical findings (Az = 0.80 ± 0.02) compared to those with complete findings (Az = 0.84 ± 0.03). The study also demonstrated the ability of the CSNN to effectively impute missing findings while performing as a predictive tool.

Duke Scholars

Author Joseph Yuan-Chieh Lo Radiology