Tuesday, October 25, 2011: 11:15 AM
Columbus Hall C-F (Hyatt Regency Chicago)
(MET) Quantitative Methods and Theoretical Developments

Yirong Wu, PhD1, David J. Vanness, Ph.D.2, Mehmet Ayvaci, MS1, Oguzhan Alagoz, PhD1 and Elizabeth S. Burnside, MD, MPH, MS1, (1)University of Wisconsin-Madison, Madison, WI, (2)Department of Population Health Sciences, Madison, WI

Purpose: To develop a maximum expected utility (MEU) model for assessing the value of diagnostic tests, and use this model to evaluate screening versus diagnostic mammography.

Method: We collected the records of 2,378 consecutive patients who underwent screening and follow-up diagnostic mammographic examinations from 2005-2008, which contained demographic risk factors and mammographic findings. Based on these features, we used a Bayesian network (BN) to estimate the risk of malignancy, constructed a receiver operating characteristic (ROC) curve using the BN estimated probabilities, and determined the optimal operating point at which expected utility was maximized. We first trained and tested two BNs (one screening and one diagnostic) using the tree augmented naïve Bayes (TAN) algorithm and 10-fold cross-validation. We generated ROC curves and calculated area under each ROC curve (AUC). Then, we assigned utility values for each category of findings (True Negative (TN), False Positive (FP), False Negative (FN) and True Positive (TP)) as follows. TN findings were chosen as our baseline and assigned a utility of zero. Based on the literature, the utility of FP was assigned a loss of ten days due to physical discomfort and anxiety. We used the previously developed and validated University of Wisconsin Breast Cancer Simulation (UWBCS) model to estimate the utility of FN as a loss of 2.52 years. We assumed the utility of TP was U(FN) × (1-α), 0≤α≤1, where α is an unknown parameter representing the overall effectiveness of breast cancer treatment. Finally, we found MEU at the optimal operating point on the ROC curve that intersected the line with slope [(U(TN)-U(FP))/(U(TP)-U(FN))] x [(1-p)/p], where p is prevalence of breast cancer.

Result: Diagnostic mammography was overall more accurate than screening mammography (AUC: 0.936 vs. 0.773, p<0.001). The MEU of both diagnostic and screening mammography increased as α increased. MEU of diagnostic mammography exceeded that of screening mammography for all values of α, with the difference approximately equal to 0.012 when α≥0.5.

Conclusion: Diagnostic mammography has higher accuracy and MEU when compared to screening mammography. Our analysis indicates that MEU methods can provide a framework to assess the value of diagnostic tests in other clinical areas, making use of the relative consequences of correct and incorrect diagnosis.