Multivariable model to predict breast cancer in non-mass enhancement lesions: a study on contrast-enhanced mammography.

Abstract

Background: To explore morphology and enhancement features of malignant non-mass enhancement (NME) lesions in contrast-enhanced mammography (CEM), and to develop a multivariable model that can accurately predict the probability of malignancy in NME lesions.

Methods: A total of 162 patients with 206 NME lesions were enrolled. The ratio of 7:3 was randomly divided into a training data set and a test data set. Differences between benign and malignant NME diseases were compared using statistical analysis in the training data set. A logistic regression analysis was used to develop a multivariable model for predicting the probability of malignancy in the training data set. The predictive value of the model was assessed by calculating the area under the curve (AUC) in both training and test data sets.

Results: The incidence of malignancy was higher in cases with malignant microcalcification (32.35%), segmental and linear distribution (55.88%), clumped and clustered ring enhancement pattern (70.59%), and Type III curve (64.71%) (all p < 0.002). The sensitivity, specificity, and AUC of the multivariable model in the training data set and the test data set were 79.41-80.77%, 94.44-97.37%, and 0.920-0.946, respectively.

Conclusions: When combining microcalcification and enhancement features, the multivariable model for CEM demonstrated acceptable sensitivity and high specificity in predicting malignant NME lesions.

Key points: Question CEM has gained momentum as an innovative and clinically useful method, but it has not been identified for the discrimination efficacy of NME lesions. Findings The multivariable model of CEM can improve the diagnostic efficiency of breast malignancy NME lesions, with acceptable sensitivity and high specificity. Clinical relevance CEM is an innovative advancement in breast imaging technology. This multivariable model of CEM integrates factors such as microcalcifications, enhancement morphological distribution, internal enhancement patterns, and time-signal intensity curves, thereby enabling accurate diagnosis of NME lesions.