Accession Number : ADA472398


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


Descriptive Note : Annual summary rept. 1 May 2006 30 Apr 2007


Corporate Author : DUKE UNIV MEDICAL CENTER DURHAM NC


Personal Author(s) : Jesneck, Jonathan


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a472398.pdf


Report Date : May 2007


Pagination or Media Count : 85


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.


Descriptors :   *BLOOD SERUM , *COMPUTER AIDED DIAGNOSIS , *BREAST CANCER , DATA BASES , MATHEMATICAL MODELS , ALGORITHMS , LINEAR SYSTEMS , NEURAL NETS , INFLAMMATION , LESIONS , MAMMOGRAPHY , BLOOD PROTEINS , CALCIFICATION , DISCRIMINATE ANALYSIS , SECONDARY , PROFILES , GAIN , CLASSIFICATION , BAYES THEOREM , RESPONSE(BIOLOGY)


Subject Categories : Medicine and Medical Research
      Computer Programming and Software


Distribution Statement : APPROVED FOR PUBLIC RELEASE