Differential diagnosis of benign and malignant male breast lesions in mammography

PURPOSE To investigate the mammographic characteristics in discriminating benign and malignant male breast lesions. METHODS Male patients with breast lesions detected by preoperative mammography were enrolled in this study from Jan 2011 to Dec 2018. All lesions were confirmed by biopsy and classified into benign group or malignant group. Imaging features included lesions location, lesion type, lesion density, lesion eccentricity, accompanying signs(calcification, nipple retraction, thickened skin and enlarged lymph nodes) were recorded and analysed by statistical methods. The AUC was calculated to assess their diagnostic performance in distinguishing benign and malignant lesions. This model was further validated by 0.632 bootstrap. RESULTS A total of 93 men(median age: 60, range 32-81 years) were enrolled, 43 patients in the benign group and 50 patients in the malignant group. In the univariate logistic analysis, age, lesion location, lesion type, lesion density, lesion eccentricity, calcification, nipple retraction and skin thickening were significantly different (p < 0.05). When the lesion showed a mass in mammography, those with a circumscribed margin were likely malignant (p < 0.05). In the multivariate logistic analysis, non-retro-areola lesions (OR: 6.900, 95 % CI: 1.413∼33.691, p < 0.05), eccentric lesions (OR: 14.566, 95 % CI: 2.800∼75.777, p < 0.05), high-density lesions (OR: 11.052, 95 % CI: 2.235∼54.666, p < 0.05), calcification (OR: 12.715 95 % CI: 1.316∼122.848, p < 0.05) and nipple retraction (OR: 24.681, 95 % CI: 2.853∼213.542 p < 0.05) were associated with breast cancer. Those variables were used to build logistic model and the AUC of the imaging model was 0.904. The imaging model was verified by 0.632 bootstrap resampling, and the AUC after 0.632 bootstrap was 0.892. CONCLUSION Mammographic characteristics could contribute to distinguishing malignant and benign male breast lesions, and the imaging model showed excellent diagnostic performance, which may help to guide clinical decision-making.