A Computer-Aided Diagnosis System for Breast Cancer Combining Mammography and Proteomics
Abstract
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 (p < 0.001). The blood serum proteins detected lesions moderately well (AUC = 0.82 for normal vs. malignant and normal vs. benign) but failed to distinguish benign from malignant lesions (AUC = 0.55), suggesting they indicate a secondary effect, such as inflammatory response, rather than a role specific for cancer.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2007
- Accession Number
- ADA472398
Entities
People
- Jonathan Jesneck
Organizations
- Duke University Hospital