Contrast-Enhanced Cone-Beam Breast CT: An Analysis of Diagnostic Value in Predicting Breast Lesion With Rim...

The objective of the current study was to investigate the diagnostic value of contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) for breast lesion with rim enhancement (RE).

All 36 patients were examined by non-contrast (NC-CBBCT) and contrast-enhanced CBBCT (CE-CBBCT) after contrast media (CM) injection. Qualitative morphological enhancement parameters and quantitative enhancement parameters were compared between malignant and benign groups. Multivariable logistic regression analysis was performed to identify independent factors that could predict breast lesion with RE malignancy. Receiver operating curve (ROC) was used to evaluate prediction performance.

A total of 36 patients with 40 lesions underwent breast CE-CBBCT were enrolled. There were significant differences in most qualitative morphological enhancement parameters between the two groups. A multivariate logistic regression model showed that △standardized HU (INR_{phase 2}-INR_preCM) [odds ratio (OR) = 1.148, 95% CI = 1.034-1.276, p = 0.01] and △standardized HU (RP_{phase 2} - RP_{phase 1}) (OR = 0.891, 95% CI = 0.814-0.976, p = 0.013) were independent indicators in predicting breast lesion with RE malignancy. △standardized HU (INR_{phase 2} - INR_preCM) combined with △standardized HU (RP_{phase 2} - RP_{phase 1}) showed significant larger area under the receiver operating curve (AUC) and higher sensitivity than each alone (p < 0.001, AUC = 0.932, sensitivity = 92.59%, specificity = 92.31%). The regression equation of the prediction model was as follows: Logit (p) = 0.351 + 0.138X × △standardized HU (INR_{phase 2} - INR_preCM) - 0.115 × △standardized HU (RP_{phase 2} - RP_{phase 1}).

With the observation of qualitative morphological enhancement parameters and the comparison of quantitative enhancement parameters of CBBCT, a reliable basis for the diagnostic accuracy in predicting breast lesion with RE could be provided. These conclusions should be verified in large, well-designed studies.