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Bayesian Network to Predict Breast Cancer Risk of Mammographic Microcalcifications and Reduce Number of Benign Biopsy Results: Initial Experience¹

¹ From the Department of Radiology, University of Wisconsin Medical School, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (E.S.B., G.A.S., W.K.L.); Section on Medical Informatics, Stanford University, Stanford, Calif (D.L.R.); Department of Statistics and Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Madison, Wis (J.P.F.); and Management Science and Engineering, Stanford University, Terman Engineering Center, Stanford, Calif (R.D.S.). Received June 29, 2005; revision requested August 31; revision received September 15; accepted October 4; final version accepted November 23. E.S.B. supported by the GE Research in Radiology Academic Fellowship. Address correspondence to E.S.B. (e-mail: es.burnside{at}hosp.wisc.edu).

Purpose: To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards.

Materials and Methods: The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiver operating characteristic (ROC) curves, and area under the ROC curve (A_z) values were compared. PPV of biopsy was also evaluated on the basis of these probability estimates.

Results: The BN and the radiologist achieved A_z values of 0.919 and 0.916, respectively, which were not significantly different. If the 34 patients estimated by the BN to have less than a 10% probability of malignancy had not undergone biopsy, the PPV of biopsy would have increased from 21.6% to 31.2% without missing a breast cancer (P < .001). At this level, the radiologist's probability estimation improved the PPV to 30.0% (P < .001).

Conclusion: A probabilistic model that includes BI-RADS descriptors for microcalcifications can distinguish between benign and malignant abnormalities at mammography as well as a breast imaging specialist can and may be able to improve the PPV of image-guided breast biopsy.

© RSNA, 2006

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