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

Elizabeth S. Burnside1,2, Daniel L. Rubin3, Ross D. Shachter4, Rita E. Sohlich5 and Edward A. Sickles1

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.



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  		Fig. 1. —Drawing shows hierarchic structure of Breast Imaging Reporting and Data System (BI-RADS) lexicon [9]. Bayesian network has 43 descriptors available for selection to convey probability of each breast disease or, when paired with a pathologic diagnosis, the probability of sampling error.

 


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  		Fig. 2. —Illustration shows structure of Bayesian network used in our study. Each finding has impact on probability of each disease of breast. Ca++ = calcifications; P/A/O = present, absent, or obscured; LN = lymph node.

 


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  		Fig. 3. —Graph shows performance of Bayesian network in determining imaging–histologic discordance. Gold standard is defined by participating radiologists. Curve allowed us to set sampling error threshold for histologic results. True-positive fraction = sensitivity, false-positive fraction = 1 – specificity.

 

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