Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4).

BACKGROUND

This study aims to providing a reliable method that has good compliance and is easy to master to improve the accuracy of NMLE diagnosis.

METHODS

This study retrospectively analyzed 122 cases of breast non-mass-like enhancement (NMLE) lesions confirmed by postoperative histology. MRI features and clinical features of benign and malignant non-mass enhancement breast lesions were compared by using independent sample t test, χtest and Fisher exact test. P < 0.05 was considered statistically significant. Statistically significant parameters were then included in logistic regression analysis to build a multiparameter differential diagnosis modelto subdivide the BI-RADS Category 4.

RESULTS

The distribution (odds ratio (OR) = 8.70), internal enhancement pattern (OR = 6.29), ADC value (OR = 5.56), and vascular sign (OR = 2.84) of the lesions were closely related to the benignity and malignancy of the lesions. These signs were used to build the MRI multiparameter model for differentiating benign and malignant non-mass enhancement breast lesions. ROC analysis revealed that its optimal diagnostic cut-off value was 5. The diagnostic specificity and sensitivity were 87.01% and 82.22%, respectively. Lesions with 1-6 points were considered BI-RADS category 4 lesions, and the positive predictive values of subtypes 4a, 4b, and 4c lesions were15.79%, 31.25%, and 77.78%, respectively.

CONCLUSIONS

Comprehensively analyzing the features of MRI of non-mass enhancement breast lesions and building the multiparameter differential diagnosis model could improve the differential diagnostic performance of benign and malignant lesions.