Significance and Application of Digital Breast Tomosynthesis for the BI-RADS Classification of Breast Cancer

  • Cai, Si-Qing (Department of Imaging, the Second Clinical College of Fujian Medical University) ;
  • Yan, Jian-Xiang (Department of Imaging, the Second Clinical College of Fujian Medical University) ;
  • Chen, Qing-Shi (Department of Imaging, the Second Clinical College of Fujian Medical University) ;
  • Huang, Mei-Ling (Department of Imaging, the Second Clinical College of Fujian Medical University) ;
  • Cai, Dong-Lu (Department of Imaging, the Second Clinical College of Fujian Medical University)
  • Published : 2015.05.18


Background: Full-field digital mammography (FFDM) with dense breasts has a high rate of missed diagnosis, and digital breast tomosynthesis (DBT) could reduce organization overlapping and provide more reliable images for BI-RADS classification. This study aims to explore application of COMBO (FFDM+DBT) for effect and significance of BI-RADS classification of breast cancer. Materials and Methods: In this study, we selected 832 patients who had been treated from May 2013 to November 2013. Classify FFDM and COMBO examination according to BI-RADS separately and compare the differences for glands in the image of the same patient in judgment, mass characteristics display and indirect signs. Employ Paired Wilcoxon rank sum test was used in 79 breast cancer patients to find differences between two examine methods. Results: The results indicated that COMBO pattern is able to observe more details in distribution of glands when estimating content. Paired Wilcoxon rank sum test showed that overall classification level of COMBO is higher significantly compared to FFDM to BI-RADS diagnosis and classification of breast (P<0.05). The area under FFDM ROC curve is 0.805, while that is 0.941 in COMBO pattern. COMBO shows relation of mass with the surrounding tissues, the calcification in the mass, and multiple foci clearly in breast cancer tissues. The optimal sensitivity of cut-off value in COMBO pattern is 82.9%, which is higher than that in FFDM (60%). They share the same specificity which is both 93.2%. Conclusions: Digital Breast Tomosynthesis (DBT) could be used for the BI-RADS classification in breast cancer in clinical.


Digital Breast Tomosynthesis(DBT);full-field digital mammography;COMBO mode;BI-RADS


  1. Ahn HS, Kim SM, Jang M, et al (2014). A new full-field digital mammography system with and without the use of an advanced post-processing algorithm: comparison of image quality and diagnostic performance. Korean J Radiol, 15, 305-312.
  2. Chan HP, Goodsitt MA, Helvie MA, et al (2014). Digital breast tomosynthesis: observer performance of clustered microcalcification detection on breast phantom images acquired with an experimental system using variable scan angles, angular increments, and number of projection views. Radiology, 7, 132722.
  3. Fidewald SM, Rafferty EA, Rose SL, et al (2014). Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA, 311, 2499-507.
  4. Gong X, Glick SJ, Liu B, Vedula AA, Thacker S (2006). A computer simulation studycomparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. Med Phys, 33, 1041-52.
  5. Greenberg JS, Javitt MC, Katzen J, Michael S, Holland AE. (2014). Clinical performance metrics of 3d digital breast tomosynthesis compared with 2d digital mammography for breast cancer screening in community practice. Am J Roentgenol, 203, 687-93.
  6. Gur D, Zuley ML, Anello MI, et al (2012). Dose reduction in digital breast tomosynthesis (DBT). screening using synthetically reconstructed projection images: an observer performance study. Acad Radil, 19, 166-71.
  7. Hakim CM, Anello MI, Cohen CS, et al (2014). Impact of and interaction between the availability of prior examinations and DBT on the interpretation of negative and benign mammograms. Acad Radiol, 21, 445-9.
  8. Lang K, Andersson I, Zackrisson S. (2014). Breast cancer detection in digital breast tomosynthesis and digital mammography-a side-by-side review of discrepant cases. Br J Radiol, 87, 20140080.
  9. Lei J, Yang P, Zhang L, Wang Y, Yang K. (2014). Diagnostic accuracy of digital breast tomosynthesis versus digital mammography for benign and malignant lesions in breasts: a meta-analysis. Eur Radiol, 24, 595-602.
  10. Liberman L, Menell JH. (2002). Breast imaging reporting and data system (BI-RADS).. Radiol Clin Noah Am, 40, 409-30.
  11. Margolies L, Cohen A, Sonnenblick E, et al (2014). Digital breast tomosynthesis changes management in patients seen at a tertiary care breast center. ISRN Radiol, 2014, 658929.
  12. Nie LY, Lu QT, Li WH, et al (2013). Sterol regulatory elementbinding protein 1 is the required for ovarian tumor growth. Oncol Rep, 30, 1346-54.
  13. Niklason LT, Christian BT, Niklason LE, et al. (1997). Digital tomosynthesis in breast imaging. Radiol, 205, 399-406.
  14. Park JM, Franken EA, Garg M, Fajardo LL, Niklason LT. (2007). Breast tomosynthesis: present considerations and future applications. Radiographies, 27, 231-40.
  15. Partyka L, Lourenco AP, Mainiero MB. (2014). Detection of mammographically occult architectural distortion on digital breast tomosynthesis screening: initial clinical experience. Am J Roentgenol, 203, 216-22.
  16. Poplaek SP, Tosteson TD, Xogd CA, Nagy HM (2007). Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography. Am J Roentgenol, 189, 616-23.
  17. Rafferty EA, Park JM, Philpotts LE, et al (2013). Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. Radiol, 266, 104-13.
  18. Rao M, Stough J, Chi YY, et al (2005). Comparison of human and automatic segmentations of kidneys from CT images. Int J Radiat Oncol Biol Phys, 61, 954-60.
  19. Reiser I, Nishikawa RM, Edwards AV, et al (2008). Automated detection ofmiemealcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys, 35, 1486-93.
  20. Roth RG, Maidment AD, Weinstein SP, Roth SD, Conant EF (2014). Digital breast tomosynthesis: lessons learned from early clinical implementation. Radiographics, 34, E89-102.
  21. Smith A (2005). Full-field breast tomosynthesis. Radiol Manage, 27, 25-31.
  22. Sun JH, Jiang L, Guo F, Zhang XS (2014). Diagnostic significance of apparent diffusion coefficient values with diffusion weighted MRI in breast cancer: a meta-analysis. Asian Pac J Cancer Prev, 15, 8271-7.
  23. Takamoto Y, Tsunoda H, Kikuchi M, et al (2013). Role of breast tomosynthesis in diagnosis of breast cancer for Japanese women. Asian Pac J Cancer Prev, 14, 3027-40.
  24. Vaughan CL, Evans MD (2012). Diagnosing breast cancer: an opportunity for innovative engineering. S Afr Med J, 102, 562-4.

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