Skip to main content

Advertisement

SpringerLink
Log in

Dedicated breast CT: state of the art—Part II. Clinical application and future outlook

  • Breast
  • Published:
European Radiology Aims and scope Submit manuscript

A Correction to this article was published on 14 February 2022

This article has been updated

Abstract

Dedicated breast CT is being increasingly used for breast imaging. This technique provides images with no compression, removal of tissue overlap, rapid acquisition, and available simultaneous assessment of microcalcifications and contrast enhancement. In this second installment in a 2-part review, the current status of clinical applications and ongoing efforts to develop new imaging systems are discussed, with particular emphasis on how to achieve optimized practice including lesion detection and characterization, response to therapy monitoring, density assessment, intervention, and implant evaluation. The potential for future screening with breast CT is also addressed.

Key Points

• Dedicated breast CT is an emerging modality with enormous potential in the future of breast imaging by addressing numerous clinical needs from diagnosis to treatment.

• Breast CT shows either noninferiority or superiority with mammography and numerical comparability to MRI after contrast administration in diagnostic statistics, demonstrates excellent performance in lesion characterization, density assessment, and intervention, and exhibits promise in implant evaluation, while potential application to breast cancer screening is still controversial.

• New imaging modalities such as phase-contrast breast CT, spectral breast CT, and hybrid imaging are in the progress of R & D.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Change history

Abbreviations

BCT:

Breast computed tomography

CBBCT:

Cone-beam breast computed tomography

CE:

Contrast-enhanced

DBT:

Digital breast tomosynthesis

MG :

Mammography

MGD :

Mean glandular dose

MRI :

Magnetic resonance imaging

NC:

Non-contrast

PCI:

Phase-contrast imaging

US:

Ultrasound

References

  1. Bleyer A, Welch HG (2012) Effect of three decades of screening mammography on breast cancer incidence. N Engl J Med 367:1998–2005

    Article  CAS  PubMed  Google Scholar 

  2. Vedantham S, Karellas A, Vijayaraghavan GR, Kopans DB (2015) Digital breast tomosynthesis: state of the art. Radiology 277:663–684

    Article  PubMed  Google Scholar 

  3. Gao Y, Heller SL (2020) Abbreviated and ultrafast breast MRI in clinical practice. Radiographics 40:1507–1527

    Article  PubMed  Google Scholar 

  4. Boone JM, Kwan ALC, Yang K, Burkett GW, Lindfors KK, Nelson TR (2006) Computed tomography for imaging the breast. J Mammary Gland Biol Neoplasia 11:103–111

    Article  PubMed  Google Scholar 

  5. O’Connell AM, Conover DL, Lin CFL (2009) Cone-beam computed tomography for breast imaging. J Radiol Nurs 28:3–11

    Article  Google Scholar 

  6. Ning R, Conover DL, Yu Y, Zhang Y, Liu S, Neugebauer J (2010) Koning cone beam breast CT for breast cancer detection, diagnosis and treatment. Am J Clin Oncol Cancer Clin Trials 33:526–527

    Google Scholar 

  7. O’Connell AM (2012) The evolution and future of dedicated breast CT. Breast diseases: a year book quarterly 23:131–133

    Google Scholar 

  8. O’Connell AM, Karellas A, Vedantham S (2014) The potential role of dedicated 3D breast CT as a diagnostic tool: review and early clinical examples. Breast J 20:592–605

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ye Z (2009) Cone-beam breast CT: a brand-new 3D breast imaging modality. The 1st Congress of Chinese Breast Radiology, May 17, 2009, Shanghai. https://d.wanfangdata.com.cn/conference/7067600. Accessed 16 Aug 2020 in Chinese

  10. Yin L, Ye Z (2016) New 3D X-ray modalities in breast imaging: digital breast tomosynthesis and cone beam breast computed tomography. Chin Med Device Inform 22:17-20 in Chinese

  11. Ye Z, Wu Y, Liu P (2017) Cone-beam breast CT diagnostic atlas. People’s Medical Publishing House, Beijing in Chinese

  12. Wienbeck S, Lotz J, Fischer U (2017) Review of clinical studies and first clinical experiences with a commercially available cone-beam breast CT in Europe. Clin Imaging 42:50–59

    Article  PubMed  Google Scholar 

  13. Kalender WA (2010) Latest development in Breast CT. Symposium Mammographicum 2010, July 11 - July 13, 2010, Liverpool. http://www.birpublications.org/doi/pdf/10.1259/conf-symp.2010. Accessed 16 Oct 2020

  14. Bärnklau-Gooriah E, Ruth V, Steiding C, Kolditz D (2020) Spiral Breast CT: an innovative technology for high resolution real 3D breast imaging without compression. DI Europe 36:70–73

    Google Scholar 

  15. Ridder K (2020) Breast CT - a ground-breaking innovation. DI Europe 36:18–21

    Google Scholar 

  16. Lindfors KK, Boone JM, Nelson TR, Yang K, Kwan AL, Miller DF (2008) Dedicated breast CT: initial clinical experience. Radiology 246:725–733

    Article  PubMed  PubMed Central  Google Scholar 

  17. O’Connell A, Conover DL, Zhang Y et al (2010) Cone-beam CT for breast imaging: radiation dose, breast coverage, and image quality. AJR Am J Roentgenol 195:496–509

    Article  PubMed  Google Scholar 

  18. O’Connell AM, Kawakyu-O’Connor D (2012) Dedicated cone-beam breast computed tomography and diagnostic mammography: comparison of radiation dose, patient comfort, and qualitative review of imaging findings in BI-RADS 4 and 5 lesions. J Clin Imaging Sci 2:7

    Article  PubMed  PubMed Central  Google Scholar 

  19. Metheany KG, Abbey CK, Packard N, Boone JM (2008) Characterizing anatomical variability in breast CT images. Med Phys 35:4685–4694

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chen L, Abbey CK, Nosratieh A, Lindfors KK, Boone JM (2012) Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies. Med Phys 39:1435–1441

    Article  PubMed  PubMed Central  Google Scholar 

  21. Vedantham S, Shi L, Glick SJ, Karellas A (2013) Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT. Med Phys 40:011901

    Article  PubMed  Google Scholar 

  22. Prionas ND, Lindfors KK, Ray S et al (2010) Contrast-enhanced dedicated breast CT: initial clinical experience. Radiology 256:714–723

    Article  PubMed  PubMed Central  Google Scholar 

  23. Han P, Ye Z (2013) Clinical application and analysis of contrast-enhanced cone-beam breast CT (CE-CBBCT) in differentiating benign and malignant breast lesions. RSNA2013, December 1 - December 6, 2013, Chicago IL. http://archive.rsna.org/2013/13020001.html. Accessed 18 Oct 2020

  24. Seifert P, Conover D, Zhang Y et al (2014) Evaluation of malignant breast lesions in the diagnostic setting with cone beam breast computed tomography (breast CT): feasibility study. Breast J 20:364–374

    Article  PubMed  Google Scholar 

  25. Prionas ND, Aminololama-Shakeri S, Yang K, Martinez SR, Lindfors KK, Boone JM (2015) Contrast-enhanced dedicated breast CT detection of invasive breast cancer preceding mammographic diagnosis. Radiol Case Rep 10:936

  26. Lindfors KK, Boone JM, Newell MS, D’Orsi CJ (2010) Dedicated breast computed tomography: the optimal cross-sectional imaging solution? Radiol Clin North Am 48:1043–1054

    Article  PubMed  PubMed Central  Google Scholar 

  27. Berger N, Marcon M, Saltybaeva N et al (2019) Dedicated breast computed tomography with a photon-counting detector: initial results of clinical in vivo imaging. Invest Radiol 54:409–418

    Article  CAS  PubMed  Google Scholar 

  28. Shen Y, Zhong Y, Lai CJ, Wang T, Shaw CC (2013) Cone beam breast CT with a high pitch (75 μm), thick (500 μm) scintillator CMOS flat panel detector: visibility of simulated microcalcifications. Med Phys 40:101915

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rößler AC, Kalender W, Kolditz D et al (2017) Performance of photon-counting breast computed tomography, digital mammography, and digital breast tomosynthesis in evaluating breast specimens. Acad Radiol 24:184–190

    Article  PubMed  Google Scholar 

  30. Aminololama-Shakeri S, Abbey CK, López JE et al (2019) Conspicuity of suspicious breast lesions on contrast enhanced breast CT compared to digital breast tomosynthesis and mammography. Br J Radiol 92:20181034

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zuley M, Sumkin J, Ganott M et al (2011) Comparison of contrast-enhanced cone beam computed tomography to contrast-enhanced magnetic resonance imaging in the categorization of breast lesions. RSNA2011, November 26 - December 2, 2011, Chicago IL. http://archive.rsna.org/2011/11003962.html. Accessed 18 Oct 2020

  32. Belair J, Zuley M, Ganott M et al (2012) Non-contrast cone-beam CT vs tomosynthesis: identification and classification of benign and malignant breast lesions. RSNA2012, November 25 - November 30, 2012, Chicago IL. http://archive.rsna.org/2012/12022690.html. Accessed 18 Oct 2020

  33. Zuley M, Guo B, Ganott M et al (2013) Comparison of visibility and diagnostic accuracy of cone beam computed tomography, tomosynthesis, MRI and digital mammography for breast masses. RSNA2013, December 1 - December 6, 2013, Chicago IL. http://archive.rsna.org/2013/13022530.html. Accessed 18 Oct 2020

  34. Zhao B, Zhang X, Cai W, Conover D, Ning R (2015) Cone beam breast CT with multiplanar and three dimensional visualization in differentiating breast masses compared with mammography. Eur J Radiol 84:48–53

    Article  PubMed  Google Scholar 

  35. Cole E, Campbell A, Vedantham S, Pisano E, Karellas A (2015) Clinical performance of dedicated breast computed tomography in comparison to diagnostic digital mammography. RSNA2015, November 29 - December 4, 2015, Chicago IL. http://archive.rsna.org/2015/15006483.html. Accessed 18 Oct 2020

  36. Aminololama-Shakeri S, Abbey CK, Gazi P et al (2016) Differentiation of ductal carcinoma in-situ from benign micro-calcifications by dedicated breast computed tomography. Eur J Radiol 85:297–303

    Article  PubMed  Google Scholar 

  37. He N, Wu YP, Kong Y et al (2016) The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: a prospective study with 212 patients. Eur J Radiol 85:392–403

    Article  PubMed  Google Scholar 

  38. Jung HK, Kuzmiak CM, Kim KW et al (2017) Potential use of American College of Radiology BI-RADS mammography atlas for reporting and assessing lesions detected on dedicated breast CT imaging: preliminary study. Acad Radiol 24:1395–1401

    Article  PubMed  Google Scholar 

  39. Wienbeck S, Uhlig J, Luftner-Nagel S et al (2017) The role of cone-beam breast-CT for breast cancer detection relative to breast densitye. Eur Radiol 27:5185–5195

    Article  PubMed  Google Scholar 

  40. Uhlig J, Fischer U, Surov A, Lotz J, Wienbeck S (2018) Contrast-enhanced cone-beam breast-CT: analysis of optimal acquisition time for discrimination of breast lesion malignancy. Eur J Radiol 99:9–16

    Article  PubMed  Google Scholar 

  41. Liu A, Ma Y, Yin L, Han P, Li H, Ye Z (2018) Comparison of the diagnostic efficiency in breast malignancy between cone beam breast CT and mammography in dense breast. Chin J Oncol 40:604–609 in Chinese

    CAS  Google Scholar 

  42. Uhlig J, Uhlig A, Kunze M et al (2018) Novel breast imaging and machine learning: predicting breast lesion malignancy at cone-beam CT using machine learning techniques. AJR Am J Roentgenol 211:W123–W131

    Article  PubMed  Google Scholar 

  43. Wienbeck S, Fischer U, Luftner-Nagel S, Lotz J, Uhlig J (2018) Contrast-enhanced cone-beam breast-CT (CBBCT): clinical performance compared to mammography and MRI. Eur Radiol 28:3731–3741

    Article  PubMed  Google Scholar 

  44. Liu A, Ma Y, Yin L, Han P, Li H, Ye Z (2018) Diagnostic value of contrast-enhanced cone beam breast CT in dense breast lesions. Chin Oncol 28:807–812 in Chinese

    Google Scholar 

  45. Uhlig J, Fischer U, Biggemann L, Lotz J, Wienbeck S (2019) Pre- and post-contrast versus post-contrast cone-beam breast CT: can we reduce radiation exposure while maintaining diagnostic accuracy? Eur Radiol 29:3141–3148

    Article  PubMed  Google Scholar 

  46. Kang W, Zhong W, Su D (2020) The cone-beam breast computed tomography characteristics of breast non-mass enhancement lesions. Acta Radiol. https://doi.org/10.1177/0284185120963923

  47. Uhlig J, Uhlig A, Biggemann L, Fischer U, Lotz J, Wienbeck S (2019) Diagnostic accuracy of cone-beam breast computed tomography: a systematic review and diagnostic meta-analysis. Eur Radiol 29:1194–1202

    Article  PubMed  Google Scholar 

  48. Zhu Y, Ma Y, Liu A, Ye Z (2020) Letter to the Editor: “Diagnostic accuracy of cone-beam breast computed tomography: a systematic review and diagnostic meta-analysis”. https://www.european-radiology.org/opinions/letter-to-the-editor-diagnostic-accuracy-of-cone-beam-breast-computed-tomography-a-systematic-review-and-diagnostic-meta-analysis. Published 15 Feb 2020. Accessed 16 Feb 2020

  49. Yin L, Ye Z (2013) Cone beam breast computed tomography (CBBCT) on breast cancer assessment. RSNA2013, December 1 - December 6, 2013, Chicago IL. http://archive.rsna.org/2013/13044241.html. Accessed 18 Oct 2020

  50. Zhao X, Su D, Kang W et al (2020) The value of cone beam breast CT in differential diagnosis of benign and malignant mass lesions. Radiol Pract 35:1268–1273 in Chinese

    Google Scholar 

  51. Caballo M, Mann R, Sechopoulos I (2018) Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging. Med Phys 45:4448–4460

    Article  PubMed  Google Scholar 

  52. Caballo M, Michielsen K, Fedon C, Sechopoulos I (2019) Towards 4D dedicated breast CT perfusion imaging of cancer: development and validation of computer simulated images. Phys Med Biol 64:245004

    Article  CAS  PubMed  Google Scholar 

  53. Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH (2008) Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology 246:116–124

    Article  PubMed  Google Scholar 

  54. Wienbeck S, Fischer U, Perske C et al (2017) Cone-beam breast computed tomography: CT density does not reflect proliferation potential and receptor expression of breast carcinoma. Transl Oncol 10:599–603

    Article  PubMed  PubMed Central  Google Scholar 

  55. Uhlig J, Fischer U, von Fintel E et al (2017) Contrast enhancement on cone-beam breast-CT for discrimination of breast cancer immunohistochemical subtypes. Transl Oncol 10:904–910

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhu Y, Zhang Y, Ma Y et al (2020) Cone-beam breast CT features associated with HER2/neu overexpression in patients with primary breast cancer. Eur Radiol 30:2731–2739

    Article  PubMed  Google Scholar 

  57. Zhu Y, Ma Y, Zhang Y, Ye Z (2021) Cone-beam breast CT features associated with intrinsic subtypes of HER2-positive breast cancer according to hormone receptor status. ECR2021 Book of Abstracts. Insights Imaging 12:S168. https://doi.org/10.1186/s13244-021-01014-5

  58. Ma Y, Liu A, O’Connell AM et al (2021) Contrast-enhanced cone beam breast CT features of breast cancers: correlation with immunohistochemical receptors and molecular subtypes. Eur Radiol 31:2580–2589

    Article  PubMed  Google Scholar 

  59. Chen JT, Zhou CY, He N, Wu YP (2020) Optimal acquisition time to discriminate between breast cancer subtypes with contrast-enhanced cone-beam CT. Diagn Interv Imaging 101:391–399

    Article  CAS  PubMed  Google Scholar 

  60. Ma W (2020) Contrast-enhanced cone-beam breast-CT (CBBCT): value in predicting lymph node involvement and prognosis for breast cancer patients. ECR2020, July 15 - July 19, 2020, Vienna. https://epos.myesr.org/poster/esr/ecr2020/C-05999. Accessed 18 Oct 2020

  61. Ma Y, Ye Z, Liu A, Yin L, Han P, Li H (2019) The accuracy of tumor size evaluation on invasive breast cancer based on cone beam breast CT. Chin J Radiol 53:286–291 in Chinese

    Google Scholar 

  62. Wienbeck S, Uhlig J, Fischer U et al (2019) Breast lesion size assessment in mastectomy specimens: correlation of cone-beam breast-CT, digital breast tomosynthesis and full-field digital mammography with histopathology. Medicine (Baltimore) 98:e17082

    Article  Google Scholar 

  63. Vedantham S, O’Connell AM, Shi L, Karellas A, Huston AJ, Skinner KA (2014) Dedicated breast CT: feasibility for monitoring neoadjuvant chemotherapy treatment. J Clin Imaging Sci 4:64

    PubMed  PubMed Central  Google Scholar 

  64. Zhong W, Kang W, Su D et al (2020) Comparative analysis of contrast-enhanced cone beam breast CT, MRI and digital mammography measure size of breast non-mass lesions. Adv Clin Exp Med 10:2387–2392 in Chinese

    Article  Google Scholar 

  65. Meng L, Su D, Zhao X et al (2020) Consistency analysis of cone beam breast CT and MRI for morphological description of breast cancer. J Clin Radiol 39:1952–1957 in Chinese

    Google Scholar 

  66. He N, Meng T, Zhou C et al (2020) Contrast enhanced cone-beam breast CT and dynamic contrast-enhanced breast MRI for evaluating residual tumor size after neoadjuvant chemotherapy in breast cancer. RSNA2020, November 29 - December 5, 2020, Chicago IL. http://archive.rsna.org/2020/20010505.html. Accessed 18 Mar 2021

  67. Boyd NF, Guo H, Martin LJ et al (2007) Mammographic density and the risk and detection of breast cancer. N Engl J Med 356:227–236

    Article  CAS  PubMed  Google Scholar 

  68. Kopans DB (2008) Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology 246:348–353

    Article  PubMed  Google Scholar 

  69. Vedantham S, Shi L, Karellas A, O’Connell AM (2012) Dedicated breast CT: fibroglandular volume measurements in a diagnostic population. Med Phys 39:7317–7328

    Article  PubMed  PubMed Central  Google Scholar 

  70. Liu A, Ye Z, Ma Y, Cao Y (2018) Reliability of breast density estimation based on cone beam breast CT. Chin J Clin Oncol 45:246–250 in Chinese

    Google Scholar 

  71. Ma Y, Cao Y, Liu A et al (2019) A reliability comparison of cone-beam breast computed tomography and mammography: breast density assessment referring to the fifth edition of the BI-RADS Atlas. Acad Radiol 26:752–759

    Article  PubMed  Google Scholar 

  72. Ducote JL, Molloi S (2009) SU-FF-I-135: breast density measurement with cone-beam CT and MRI: a post mortem study. Med Phys 36:2466

    Google Scholar 

  73. Johnson T, Ding H, Le HQ, Ducote JL, Molloi S (2013) Breast density quantification with cone-beam CT: a post-mortem study. Phys Med Biol 58:8573–8591

    Article  PubMed  PubMed Central  Google Scholar 

  74. Ding H, Johnson T, Lin M, Su L, Molloi S (2013) TH-A-103-11: breast density measurement with cone-beam CT and MRI: a postmortem study. Med Phys 40:528

    Article  Google Scholar 

  75. Seifert PJ, Morgan RC, Conover DL, Arieno AL (2017) Initial experience with a cone-beam breast computed tomography-guided biopsy system. J Clin Imaging Sci 7:1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Wienbeck S, Lotz J, Fischer U (2017) Feasibility of vacuum-assisted breast cone-beam CT-guided biopsy and comparison with prone stereotactic biopsy. AJR Am J Roentgenol 208:1154–1162

    Article  PubMed  Google Scholar 

  77. Zheng Z, Kang W, Zhao X, Meng L, Liu Y (2020) Cone-beam breast CT-guided needle biopsy in diagnosis of breast invasive ductal carcinoma: case report. Chin J Interv Imaging Ther 17:319 in Chinese

    Google Scholar 

  78. Meng L (2020) Comparative analysis of immunohistochemical detection indexes and molecular subtypes with breast specimens acquired with cone beam breast computed tomography-guided core needle biopsy, ultrasound-guided core needle biopsy and surgical resection. RSNA2020, November 29 - December 5, 2020, Chicago IL. http://archive.rsna.org/2020/20010810.html. Accessed 18 Mar 2021

  79. Zheng Z, Kang W, Su D (2012) Progresses in biopsy of breast cancer guided by different imaging techniques. Chin J Med Imaging Technol 35:1590–1593 in Chinese

    Google Scholar 

  80. Prionas ND, McKenney SE, Stern RL, Boone JM (2012) Kilovoltage rotational external beam radiotherapy on a breast computed tomography platform: a feasibility study. Int J Radiat Oncol Biol Phys 84:533–539

    Article  PubMed  PubMed Central  Google Scholar 

  81. Couto LS, Freitas-Junior R, Correa RS et al (2019) Mean glandular dose in digital mamography in women with breast implants. J Radiol Prot 39:498–510

    Article  CAS  PubMed  Google Scholar 

  82. Ruby L, Shim S, Berger N, Marcon M, Frauenfelder T, Boss A (2020) Diagnostic value of a spiral breast computed tomography system equipped with photon counting detector technology in patients with implants: an observational study of our initial experiences. Medicine (Baltimore) 99:e20797

    Article  Google Scholar 

  83. Boone JM, Lindfors KK (2006) Breast CT: potential for breast cancer screening and diagnosis. Future Oncol 2:351–356

    Article  PubMed  Google Scholar 

  84. Ye Z (2015) Breast cancer screening: looking forward to new technology amid controversy. Chin Comput Med Imaging 21:418 in Chinese

    Google Scholar 

  85. Aminololama-Shakeri S, Hargreaves JB, Boone JM, Lindfors KK (2016) Dedicated breast CT: screening technique of the future. Curr Breast Cancer Rep 8:242–247

    Article  Google Scholar 

  86. Vaughan CL (2019) Novel imaging approaches to screen for breast cancer: recent advances and future prospects. Med Eng Phys 72:27–37

    Article  PubMed  PubMed Central  Google Scholar 

  87. Ruile G, Djanatliev A, Kriza C et al (2015) Screening for breast cancer with breast-CT in a ProHTA simulation. J Comp Eff Res 4:553–567

    Article  PubMed  Google Scholar 

  88. Berger N, Marcon M, Frauenfelder T, Boss A (2020) Dedicated spiral breast computed tomography with a single photon-counting detector: initial results of the first 300 women. Invest Radiol 55:68–72

    Article  PubMed  Google Scholar 

  89. Lee TC, Reyna C, Shaughnessy E, Lewis JD (2019) Screening of populations at high risk for breast cancer. J Surg Oncol 120:820–830

    Article  PubMed  Google Scholar 

  90. Pijpe A, Andrieu N, Easton DF et al (2012) Exposure to diagnostic radiation and risk of breast cancer among carriers of BRCA1/2 mutations: retrospective cohort study (GENE-RAD-RISK). BMJ 345:e5660

    Article  PubMed  PubMed Central  Google Scholar 

  91. Auweter SD, Herzen J, Willner M et al (2014) X-ray phase-contrast imaging of the breast--advances towards clinical implementation. Br J Radiol 87:20130606

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Tavakoli Taba S, Gureyev TE, Alakhras M, Lewis S, Lockie D, Brennan PC (2018) X-ray phase-contrast technology in breast imaging: principles, options, and clinical application. AJR Am J Roentgenol 211:133–145

    Article  Google Scholar 

  93. Fiedler S, Bravin A, Keyriläinen J et al (2004) Imaging lobular breast carcinoma: comparison of synchrotron radiation DEI-CT technique with clinical CT, mammography and histology. Phys Med Biol 49:175–188

    Article  CAS  PubMed  Google Scholar 

  94. Sztrókay A, Herzen J, Auweter SD et al (2013) Assessment of grating-based X-ray phase-contrast CT for differentiation of invasive ductal carcinoma and ductal carcinoma in situ in an experimental ex vivo set-up. Eur Radiol 23:381–387

    Article  PubMed  Google Scholar 

  95. Grandl S, Willner M, Herzen J et al (2014) Visualizing typical features of breast fibroadenomas using phase-contrast CT: an ex-vivo study. PLoS One 9:e97101

    Article  PubMed  PubMed Central  Google Scholar 

  96. Baran P, Mayo S, McCormack M et al (2018) High-resolution X-ray phase-contrast 3-D imaging of breast tissue specimens as a possible adjunct to histopathology. IEEE Trans Med Imaging 37:2642–2650

    Article  PubMed  Google Scholar 

  97. Hellerhoff K, Birnbacher L, Sztrókay-Gaul A et al (2019) Assessment of intraductal carcinoma in situ (DCIS) using grating-based X-ray phase-contrast CT at conventional X-ray sources: an experimental ex-vivo study. PLoS One 14:e0210291

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Li X, Gao H, Chen Z et al (2018) Diagnosis of breast cancer based on microcalcifications using grating-based phase contrast CT. Eur Radiol 28:3742–3750

    Article  PubMed  Google Scholar 

  99. Brombal L, Arfelli F, Delogu P et al (2019) Image quality comparison between a phase-contrast synchrotron radiation breast CT and a clinical breast CT: a phantom based study. Sci Rep 9:17778

    Article  PubMed  PubMed Central  Google Scholar 

  100. Pacilè S, Dullin C, Baran P et al (2019) Free propagation phase-contrast breast CT provides higher image quality than cone-beam breast-CT at low radiation doses: a feasibility study on human mastectomies. Sci Rep 9:13762

    Article  PubMed  PubMed Central  Google Scholar 

  101. Tavakoli Taba S, Baran P, Nesterets YI et al (2020) Comparison of propagation-based CT using synchrotron radiation and conventional cone-beam CT for breast imaging. Eur Radiol 30:2740–2750

    Article  PubMed  Google Scholar 

  102. Pacilè S, Baran P, Dullin C et al (2018) Advantages of breast cancer visualization and characterization using synchrotron radiation phase-contrast tomography. J Synchrotron Radiat 25:1460–1466

    Article  PubMed  Google Scholar 

  103. Ding H, Klopfer MJ, Ducote JL, Masaki F, Molloi S (2014) Breast tissue characterization with photon-counting spectral CT imaging: a postmortem breast study. Radiology 272:731–738

    Article  PubMed  Google Scholar 

  104. Ruth V, Kolditz D, Steiding C, Kalender WA (2020) Investigation of spectral performance for single-scan contrast-enhanced breast CT using photon-counting technology: a phantom study. Med Phys 47:2826–2837

    Article  CAS  PubMed  Google Scholar 

  105. Shah JP, Mann SD, McKinley RL, Tornai MP (2017) Implementation and CT sampling characterization of a third-generation SPECT-CT system for dedicated breast imaging. J Med Imaging (Bellingham) 4:033502

    Article  Google Scholar 

  106. Raylman RR, Van Kampen W, Stolin AV et al (2018) A dedicated breast-PET/CT scanner: evaluation of basic performance characteristics. Med Phys 45:1603–1613

    Article  PubMed  Google Scholar 

  107. Reiser I, Nishikawa RM, Giger ML, Boone JM, Lindfors KK, Yang K (2012) Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys 39:866–873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Wang X, Nagarajan MB, Conover D, Ning R, O’Connell A, Wismüller A (2014) Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT. Proc SPIE Int Soc Opt Eng 9038:903822

    PubMed  PubMed Central  Google Scholar 

  109. Caballo M, Teuwen J, Mann RM, Sechopoulos I (2019) Computer-aided detection of breast masses in dedicated breast CT images using adaptive parenchyma local search and deep learning. ECR2019 Book of Abstracts. Insights Imaging 10:S508. https://doi.org/10.1186/s13244-019-0713-y

  110. Kuo H, Giger M, Reiser I et al (2014) Development of a new 3D spiculation feature for enhancing computerized classification on dedicated breast CT. RSNA2014, November 30 - December 5, Chicago IL. http://archive.rsna.org/2014/14008189.html. Accessed 18 Oct 2020

  111. Lee J, Nishikawa RM, Reiser I, Boone JM, Lindfors KK (2015) Local curvature analysis for classifying breast tumors: preliminary analysis in dedicated breast CT. Med Phys 42:5479–5489

    Article  PubMed  PubMed Central  Google Scholar 

  112. Caballo M, Pangallo DR, Sanderink W et al (2021) Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging. Med Phys 48:313–328

    Article  PubMed  Google Scholar 

  113. Ma Y, Zhang Y, Liu A, Zhu Y, Ye Z (2019) Radiomics analysis on cone-beam breast CT: prediction of breast cancer immunohistochemical subtypes. CCR2019, November 11 - November 13, Beijing. https://ccr2019.medmeeting.org/cn. Accessed 16 Jan 2020

  114. Zhu Y, Zhang Y, Ma Y, Ye Z (2020) Parenchymal radiomics in cone-beam breast CT: comparison with mammography and implication for cancer risk estimation. ECR2020 Book of Abstracts. Insights Imaging 11:S509. https://doi.org/10.1186/s13244-020-00851-0

  115. Esserman LJ, Kumar AS, Herrera AF et al (2006) Magnetic resonance imaging captures the biology of ductal carcinoma in situ. J Clin Oncol 24:4603–4610

    Article  PubMed  Google Scholar 

  116. Kuhl CK, Schrading S, Bieling HB et al (2007) MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet 370:485–492

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by National Key R&D Program of China (No. 2017YFC0112600, 2017YFC0112601, 2017YFC0112602, 2017YFC0112603, 2017YFC0112604, 2017YFC0112605, 2017YFC0109300, 2017YFC0109301, 2017YFC0109302, 2017YFC0109303, 2017YFC0109304), National Natural Science Foundation of China (No. 81571671), Tianjin Science and Technology Major Project (No. 19ZXDBSY00080), and Key Project of Tianjin Medical Industry (No. 16KG130).

Funding

This study has received funding from National Key R&D Program of China (No. 2017YFC0112600, 2017YFC0112601, 2017YFC0112602, 2017YFC0112603, 2017YFC0112604, 2017YFC0112605, 2017YFC0109300, 2017YFC0109301, 2017YFC0109302, 2017YFC0109303, 2017YFC0109304), National Natural Science Foundation of China (No. 81571671), Tianjin Science and Technology Major Project (No. 19ZXDBSY00080), and Key Project of Tianjin Medical Industry (No. 16KG130).

Author information

Authors and Affiliations

  1. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Huan-Hu-Xi Road, Ti-Yuan-Bei, Hexi District, 300060, Tianjin, China

    Yueqiang Zhu, Yue Ma, Aidi Liu, Haijie Li, Yuwei Zhang & Zhaoxiang Ye

  2. Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Avenue, Box 648, Rochester, NY, 14642, USA

    Avice M. O’Connell

  3. Koning Corporation, Lennox Tech Enterprise Center, 150 Lucius Gordon Drive, Suite 112, West Henrietta, NY, 14586, USA

    Xiaohua Zhang

Authors
  1. Yueqiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avice M. O’Connell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aidi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haijie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaohua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhaoxiang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoxiang Ye.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Zhaoxiang Ye.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Koning Corporation.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because it is a Review article.

Ethical approval

Institutional Review Board approval was not required because it is a Review article.

Study subjects or cohorts overlap

Study subjects or cohorts have been previously reported, as this is a literature review.

Methodology

• retrospective

• review

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: Modifications have been made to Table 1, Table 2, Table 3, Figure 2, a sentence in section “Histopathology prediction” and references 25 and 110. Full information regarding the corrections made can be found in the erratum/correction for this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, Y., O’Connell, A.M., Ma, Y. et al. Dedicated breast CT: state of the art—Part II. Clinical application and future outlook. Eur Radiol 32, 2286–2300 (2022). https://doi.org/10.1007/s00330-021-08178-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08178-0

Keywords

Search

Navigation