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A Probabilistic Expert System That Provides Automated Mammographic–Histologic Correlation: Initial Experience

¹ Department of Radiology, University of California School of Medicine, Box 1667, San Francisco, CA 94143-1667.
² Present address: Department of Radiology, University of Wisconsin Medical School, Breast Care Center, G3/101, 600 Highland Ave., Madison, WI 53792-3252.
³ Stanford Medical Informatics, Stanford University School of Medicine, Medical School Office Bldg., X-215, 251 Campus Dr., Stanford, CA 94305-5479.
⁴ Department of Management Science and Engineering, Stanford University, Terman Engineering Center, Stanford, CA 94305-4026.
⁵ Marin Breast Health Center, 1240 S Eliseo St., Ste. 101, Greenbrae, CA 94904.

OBJECTIVE. We sought to determine whether a probabilistic expert system can provide accurate automated imaging–histologic correlations to aid radiologists in assessing the concordance of mammographic findings with the results of imaging-guided breast biopsies.

MATERIALS AND METHODS. We created a Bayesian network in which Breast Imaging Reporting and Data System (BI-RADS) descriptors are used to convey the level of suspicion of mammographic abnormalities. Our system is a computer model that links BI-RADS descriptors with diseases of the breast using probabilities derived from the literature. Mammographic findings are used to update pretest probabilities (prevalence of disease) into posttest probabilities applying Bayes' theorem. We evaluated the histologic results of 92 consecutive imaging-guided breast biopsies for concordance with the mammographic findings during radiology–pathology review sessions. First, radiologists with no knowledge of the biopsy results chose BI-RADS descriptors for the mammographic findings. After the histologic diagnosis was revealed, the radiologists assessed concordance between the pathologic results and the mammographic findings. We then input the information gathered from these sessions into the Bayesian network to produce an automated mammographic–histologic correlation.

RESULTS. We had a sampling error rate of 1.1% (1/92 biopsies). Our expert system was able to integrate pathologic diagnoses and mammographic findings to obtain probabilities of sampling error, thereby enabling us to identify the incorrect pathologic diagnosis with 100% sensitivity while maintaining a specificity of 91%.

CONCLUSION. Our probabilistic expert system has the potential to help radiologists in identifying breast biopsy results that are discordant with mammographic findings and discovering cases in which biopsy sampling errors may have occurred.

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