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Rationale and Objective: The objective of this study is to test the hypothesis that there are patterns in erroneous assessment of BI-RADS features among radiology trainees when interpreting mammographic masses and that these patterns can be captured in individualized statistical user models. Identifying these patterns could be useful in personalizing and adapting educational material to complement the individual weaknesses of each trainee during his or her mammography education. Materials and Methods: Reading data of 33 mammographic cases containing masses was used. The cases were individually described by 10 radiology residents using four BI-RADS features: mass shape, mass margin, mass density and parenchyma density. For each resident, an individual model was automatically constructed that predicts likelihood (HIGH or LOW) of erroneously assigning each BI-RADS descriptor by the resident. Error was defined as deviation of the resident's assessment from the expert assessments. We evaluated the predictive performance of the models using leave-one-out crossvalidation. Results: The user models were able to predict which assessments have higher likelihood of error. The proportion of actual errors to the number of situations in which these errors could potentially occur was significantly higher (P < .05) when user-model assigned HIGH likelihood of error than when LOW likelihood of error was assigned for three of the four BI-RADS features. Overall, the difference between the HIGH and LOW likelihood of error groups was statistically significant (P < .0001) combining all four features. Conclusion: Error making in BI-RADS descriptor assessment appears to follow patterns that can be captured with statistical pattern recognition-based user models. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: In our earlier studies, we reported an evidence-based computer-assisted decision (CAD) system for location-specific interrogation of mammograms. A content-based image retrieval framework with information theoretic (IT) similarity measures serves as the foundation for this system. Specifically, the normalized mutual information (NMI) was shown to be the most effective similarity measure for reduction of false-positive marks generated by other prescreening mass detection schemes. The objective of this work was to investigate the importance of image filtering as a possible preprocessing step in our IT-CAD system., Materials and Methods: Different filters were applied, each one aiming to compensate for known limitations of the NMI similarity measure. The study was based on a region-of-interest database that included true masses and false-positive regions from digitized mammograms., Results: Receiver-operating characteristics (ROC) analysis showed that IT-CAD is affected slightly by image filtering. Modest, yet statistically significant, performance gain was observed with median filtering (overall ROC area index A(z) improved from 0.78 to 0.82). However, Gabor filtering improved performance for the high-sensitivity portion of the ROC curve where a typical false-positive reduction scheme should operate (partial ROC area index (0.90)A(z) improved from 0.33 to 0.37). Fusion of IT-CAD decisions from different filtering schemes markedly improved performance (A(z) = 0.90 and (0.90)A(z) = 0.55). At 95% sensitivity, the system's specificity improved by 36.6%., Conclusions: Additional improvement in false-positive reduction can be achieved by incorporating image filtering as a preprocessing step in our IT-CAD system.

Rationale and Objectives: The authors improved the noninvasive diagnosis of acute pulmonary embolism (PE) by studying the clinical and chest radiographic findings of patients suspected of having PE and correlating those findings with the physicians' clinical impression., Methods: A stepwise linear discriminant algorithm was developed on the basis of 1,064 patients from the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) study to select clinical and chest radiographic findings with the highest diagnostic power in patients suspected of having PE. Subsequently, a linear classifier and a nonlinear artificial neural network were developed to help diagnose PE on the basis of the reduced number of findings., Results: Both classifiers produced a statistically significant improvement (Az = 0.77 +/- 0.02) in the clinical performance of the PIOPED physicians (Az = 0.72 +/- 0.02). Results are also presented separately for groups of patients classified on the basis of the difficulty level of their ventilation-perfusion lung scans., Conclusion: Two computer-aided diagnostic tools were developed to assist physicians in the assessment of the pretest likelihood of PE by using an optimally reduced number of findings.