The cone-beam breast computed tomography characteristics of breast non-mass enhancement lesions.

BACKGROUND: Cone-beam computed tomography (CBBCT) of the breast is emerging as a way of improving breast cancer diagnostic yield.

PURPOSE: To find characteristics of non-mass enhancement (NME) lesions on breast CBBCT and to identify the characteristics that distinguish malignant and benign lesions.

MATERIAL AND METHODS: Breast CBBCT images of 84 NME lesions were analyzed. Internal enhancement distribution and patterns, calcification distribution and suspicious morphology, and ΔHU enhancement values were compared between post-contrast and pre-contrast malignant and benign lesions. Univariate analyses were applied to find the strongest indicators of malignancy, and logistic regression analysis was used to develop a fitting equation for the combined diagnostic model.

RESULTS: In the 84 NME lesions, the indicators of malignancy were as follows: segmental enhancement distribution ( P  = 0.011, 53.62% sensitivity, 86.67% specificity, 94.87% positive predictive value [PPV], and 28.89% negative predictive value [NPV]), clumped internal enhancement patterns ( P  = 0.017, 50.72% sensitivity, 86.67% specificity, 94.59% PPV, and 27.66% NPV), ΔHU ≥ 93.57 Hounsfield units (HU) ( P  = 0.004, 66.67% sensitivity, 73.33% specificity, 92.00% PPV, and 32.35% NPV), and NME lesions with calcification ( P  = 0.002, 36.23% sensitivity, 20.00% specificity, 82.14% PPV, and 67.57% NPV). The fitting equation for the combined diagnostic model was as follows: Logit (P) = -0.579 +1.318 × enhancement distribution + 1.000 × internal enhancement patterns + 1.539 × ΔHU value + 1.641 ×NME type.

CONCLUSION: Individual diagnostic criteria based on breast CBBCT characteristics (segmental enhancement distribution, clumped internal enhancement patterns, ΔHU values > 93.57 HU, and NME lesions with calcification) had high specificity and PPV; when combined, they had high sensitivity in predicting malignant NME lesions.