BI-RADS lesion characteristics predict likelihood of malignancy in breast MRI for masses but not for nonmasslike enhancement.
OBJECTIVE
The purpose of our study was to evaluate the predictive features of BI-RADS lesion characteristics and the risk of malignancy for mammographically and clinically occult lesions detected initially on breast MRI.


METHODS
We reviewed 1,523 consecutive breast MRI examinations performed from January 1, 2003, to June 30, 2005, to identify all lesions initially detected on MRI and assessed as BI-RADS 4 or 5 for which the patient underwent subsequent imaging-guided needle or excisional biopsy. BI-RADS lesion features were recorded for each case, and the risk of malignancy was assessed using generalized estimating equations. Separate multivariate models were constructed for lesions classified as masses.


RESULTS
Included in the analysis were 258 suspicious lesions in 196 women. Among all lesions, those of 1 cm or greater were significantly more often malignant (50/147, 34%) than lesions of less than 1 cm (22/111, 20%; odds ratio, 2.09; 95% CI, 1.13-3.83). For masses, size, BI-RADS margin, and enhancement pattern predicted malignancy. In multivariate analysis of combinations of features, masses of 1 cm or greater with heterogeneous enhancement and irregular margins had a 68% probability of malignancy. Masses of 1 cm or greater with smooth margins and homogeneous enhancement had the lowest predicted probability of malignancy of 3%. BI-RADS descriptors and size were not significant predictors of malignancy for nonmasslike enhancement (NMLE).


CONCLUSIONS
Combinations of BI-RADS lesion descriptors can predict the probability of malignancy for breast MRI masses but not for NMLE. If our model is validated, masses with a low probability of malignancy may be eligible for short-interval follow-up rather than biopsy. Further research focused on predictive features of NMLE is needed.
Gutierrez
Robert L
RL
Department of Radiology, University of Washington Medical Center, University of Washington, Seattle, WA. rgut@u.washington.edu
DeMartini
Wendy B
WB
Eby
Peter R
PR
Kurland
Brenda F
BF
Peacock
Sue
S
Lehman
Constance D
CD
D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
C064925:gadodiamide; D019786:Gadolinium DTPA