目的: 探讨乳腺钼靶和高频彩超对乳腺微钙化病灶的诊断效能。方法: 回顾2017年6月1日至2022年5月31日期间,在江门市妇幼保健院行乳腺钼靶检查发现乳腺微钙化而行钙化灶活检的患者,分析其一般特征、病理良恶性分布情况、病理类型以及超声和钼靶对乳腺微钙化预测为乳腺癌的诊断效能。结果: 共有160例乳腺微钙化患者接受手术治疗,平均年龄为(38.00±8.80)岁,单发病灶有75例(46.87%),恶性有34例(21.25%);最常见的良性钙化病理类型为乳腺腺病/囊性增生,占比60.0%;最常见的恶性钙化病理类型为导管原位癌,占比11.87%。良性病变最常见的钙化类型是圆形/短棒状钙化,占比为47.50%;恶性病变最常见的钙化类型为区域性簇状钙化,占比为9.38%。高频乳腺彩超对乳腺恶性钙化诊断的准确率为63.13%,特异度为88.50%;而钼靶对乳腺恶性钙化诊断的准确率为29.38%,特异度为93.33%。结论: 对于乳腺微钙化患者钼靶和彩超各有优势、互相补充,临床上应常规对乳腺微钙化患者施行这两项检查,以便于更准确地进行术前评估和定位。
Objective: To compare the diagnostic efficacy between mammography and high frequency ultrasonography in the examination of breast microcalcifications. Methods: From June 1, 2017 to May 31, 2022, patients who underwent calcification biopsy due to breast microcalcification by mammography in Jiangmen Maternity and Child Health Care Hospital were reviewed. The characteristics, pathological results of the breast microcalcification patients were analyzed. The diagnostic efficacy of mammography and high frequency ultrasonography in the examination of breast microcalcifications were compared. Results: A total of 160 cases of patients with breast microcalcifications were given surgical treatment. The average age of the patients was 38.00+8.80 years. There were 75 cases (46.87%) of single-side lesions and 34 cases (21.25%) of malignant lesions. The most common pathological type of benign calcification was breast adenosis/cystic hyperplasia, accounting for 60%. The most common pathological type of malignant calcification was ductal carcinoma in situ, accounting for 11.87%. The most common type of calcification in benign lesions was round/short rod calcification, accounting for 47.50%. The most common type of calcification in malignant lesions was regional cluster calcification, accounting for 9.38%. The accuracy and specificity of high-frequency ultrasonography in the diagnosis of malignant calcification of breast were 63.13% and 88.50% respectively, and they were 29.38% and 93.33% respectively for mammography. Conclusion: Mammography and high-frequency ultrasonography both have their own advantages in diagnosing breast microcalcifications. In clinical practice, these two examinations should be routinely performed for patients with breast microcalcifications to make more accurate preoperative evaluation and positioning.
[1] 陈穹, 潘鑫, 侯羽宇, 等. 数字化乳腺断面合成技术和全视野数字化乳腺X线摄影对乳腺病变钙化的检出和诊断效能研究[J]. 中华放射学杂志, 2020, 54(9): 864-868.
[2] 孙博, 李军楠, 青春, 等. 60岁以上乳腺癌的乳腺X线影像学表现及病灶微钙化与年龄的相关性研究[J]. 中国肿瘤临床, 2020, 47(4): 193-197.
[3] 历凤云. 乳腺X线成像对早期乳腺癌诊断的分析[J]. 影像研究与医学应用, 2020, 4(10): 58-59.
[4] 朱丽钰, 娄鉴娟, 王思奇, 等. 乳腺导管原位癌微钙化X线特征与病理的对照研究[J]. 放射学实践, 2019, 34(11): 1237-1241.
[5] 张燕玲, 秦姝竹. 微钙化乳腺癌在钼靶片诊断中的价值和进展分析[J]. 影像研究与医学应用, 2020, 4(21): 5-7.
[6] 康姝, 王学梅, 耿晶. 高频超声显示乳腺微小钙化灶[J]. 中国医学影像技术, 2009, 25(1): 100-102.
[7] Farvid MS, Stern MC, Norat T, et al. Consumption of red and processed meat and breast cancer incidence: A systematic review and meta‐analysis of prospective studies[J]. Int J Cancer, 2018, 143(11): 2787-2799.
[8] Sopik V. International variation in breast cancer incidence and mortality in young women[J]. Breast Cancer Res Treat, 2021, 186(2): 497-507.
[9] Lei SY, Zheng RS, Zhang SW, et al. Breast cancer incidence and mortality in women in China: temporal trends and projections to 2030[J]. Cancer Biol Med, 2021, 18(3): 900-908.
[10] Ouyang YL, Zhou ZH, Wu WW, et al. A review of ultrasound detection methods for breast microcalcification[J]. Math Biosci Eng, 2019, 16(4): 1761-1785.
[11] Cai H, Huang Q, Rong W, et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms[J]. Comput Math Methods Med, 2019, 3(3): 1-10.
[12] Bonfiglio R, Scimeca M, Urbano N, et al. Breast microcalcifications: Biological and diagnostic perspectives[J]. Future Oncol, 2018, 14(30): 3097-3099.
[13] Henrot P, Leroux A, Barlier C, et al. Breast microcalcifications: the lesions in anatomical pathology[J]. Diagn Interv Imaging, 2014, 95(2): 141-152.
[14] Bonfiglio R, Scimeca M, Toschi N, et al. Radiological, histological and chemical analysis of breast microcalcifications: diagnostic value and biological significance[J]. J Mammary Gland Biol Neoplasia, 2018, 23(1): 89-99.
[15] Schnenberger C, Hejduk P, Ciritsis A, et al. Classification of mammographic breast microcalcifications using a deep convolutional neural network: A BI-RADS–based approach[J]. Invest Radiol, 2021, 56(4): 224-231.
[16] Fan L, Strasser-Weippl K, Li JJ, et al. Breast cancer in China[J]. Lancet Oncol, 2014, 15(7): e279-89.
[17] 唐利立, 廖妮. 高频B超在中国妇女致密型乳腺的乳腺癌筛查中的应用[J]. 中国普通外科杂志, 2006, 15(10): 732-735.
