Computer-Aided Diagnosis System for Breast Cancer Combining Mammography and Proteomics

摘要

This study investigated a computer-aided diagnosis system for breast cancer by combining the following three data sources: mammogram films, radiologist-interpreted BI-RADS descriptors, and proteomic profiles of blood sera. We implemented under 100-fold cross-validation various classification algorithms, including Bayesian probit regression, iterated Bayesian model averaging, linear discriminant analysis, artificial neural networks, as well as a novel method of decision fusion. The top-performing classifier, decision fusion achieved AUC = 0.85 0.01 on the calcification data set and 0.94 0.01 on the mass data set. Decision fusion had a slight performance gain over the ANN and LDA (p = 0.02), but was comparable to Bayesian probit regression. Decision fusion significantly outperformed the other classifiers.