BI-RADS lesion characteristics predict likelihood of malignancy in breast MRI for masses but not for nonmasslike enhancement

AJR Am J Roentgenol. 2009 Oct;193(4):994-1000. doi: 10.2214/AJR.08.1983.

Abstract

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.

Materials and 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).

Conclusion: 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.

MeSH terms

  • Aged
  • Algorithms*
  • Breast Neoplasms / diagnosis*
  • Female
  • Gadolinium DTPA*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Likelihood Functions
  • Magnetic Resonance Imaging / methods*
  • Middle Aged
  • Reproducibility of Results
  • Sensitivity and Specificity

Substances

  • gadodiamide
  • Gadolinium DTPA