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Breast Cancer

, Volume 23, Issue 2, pp 301–309 | Cite as

Targeted sonography using an image fusion technique for evaluation of incidentally detected breast lesions on chest CT: a pilot study

  • Junko Kousaka
  • Shogo NakanoEmail author
  • Takahito Ando
  • Rie Tetsuka
  • Kimihito Fujii
  • Miwa Yoshida
  • Yukako Shiomi-Mouri
  • Manami Goto
  • Yuko Imai
  • Tsuneo Imai
  • Takashi Fukutomi
  • Eisuke Katsuda
  • Tsuneo Ishiguchi
  • Osamu Arai
Original Article

Abstract

Background

With increasing use of computed tomography (CT), incidentally detected breast lesions are being encountered more frequently. The aim of our study was to verify the utility of targeted sonography using an image fusion technique, real-time virtual sonography (RVS) that coordinates real-time sonography images with previously obtained CT images using a magnetic position tracking system, for evaluation of incidentally detected breast lesions on chest CT.

Methods

Eleven lesions in 11 women with no history of breast cancer who were referred to our unit for assessment of breast lesions incidentally detected on CT were enrolled in this study. To assess the efficacy of targeted sonography using RVS, we analyzed the frequency of sonographic detection of incidentally detected breast lesions and the difference between sonography- and CT-determined diameters.

Results

Using RVS guidance, all 11 lesions were sonographically detected. Ten (91 %) of 11 lesions underwent sonography-guided biopsy, yielding a success rate of 90 % (9/10). The remaining sonography-guided biopsy failure lesion required surgical biopsy for definitive diagnosis; this was performed after RVS was used to mark CT imaging information onto the breast surface. Four (36 %) lesions subsequently proved to be malignant. The mean diameters provided by RVS were 14.9 ± 6.7 mm for sonography and 16.8 ± 7.5 mm for CT (p = 0.538).

Conclusion

Using RVS, a sonographic probe was precisely guided to the lesions. Our results suggest that targeted sonography using RVS is a useful technique for identifying incidentally detected breast lesions on chest CT.

Keywords

Breast cancer Computed tomography Sonography Real-time virtual sonography Incidentally detected breast lesion 

Notes

Acknowledgments

This study was supported by the Matching Fund Subsidy for Private Universities from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Grants-in-Aid for Scientific Research (22591445, 24591922, 25461852, and 25461999) in Japan.

Conflict of interest

None.

References

  1. 1.
    Hussain A, Gordon-Dixon A, Almusawy H, Sinha P, Desai A. The incidence and outcome of incidental breast lesions detected by computed tomography. Ann R Coll Surg Engl. 2010;92:124–6.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Lin WC, Hsu HH, Li CS, Yu JC, Hsu GC, Yu CP, et al. Incidentally detected enhancing breast lesions on chest computed tomography. Korean J Radiol. 2011;12:44–51.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Shojaku H, Seto H, Iwai H, Kitazawa S, Fukushima W, Saito K. Detection of incidental breast tumors by noncontrast spiral computed tomography of the chest. Radiat Med. 2008;26:362–7.CrossRefPubMedGoogle Scholar
  4. 4.
    Swensen SJ, Jett JR, Hartman TE, Midthun DE, Sloan JA, Sykes AM, et al. Lung cancer screening with CT: mayo clinic experience. Radiology. 2003;226:756–61.CrossRefPubMedGoogle Scholar
  5. 5.
    Nakano S, Yoshida M, Fujii K, Yorozuya K, Mouri Y, Kousaka J, et al. Fusion of MRI and sonography image for breast cancer evaluation using real-time virtual sonography with magnetic navigation: first experience. Jpn J Clin Oncol. 2009;39:552–9.CrossRefPubMedGoogle Scholar
  6. 6.
    Chang JM, Han W, Moon HG, Yi A, Cho N, Koo HR, et al. Evaluation of tumor extent in breast cancer patients using real-time MR navigated ultrasound: preliminary study. Eur J Radiol. 2012;81:3208–15.CrossRefPubMedGoogle Scholar
  7. 7.
    Fausto A, Rizzatto G, Preziosa A, Gaburro L, Washburn MJ, Rubello D, et al. A new method to combine contrast-enhanced magnetic resonance imaging during live ultrasound of the breast using volume navigation technique: a study for evaluating feasibility, accuracy and reproducibility in healthy volunteers. Eur J Radiol. 2012;81:e332–7.CrossRefPubMedGoogle Scholar
  8. 8.
    Futamura M, Morimitsu K, Nawa M, Kanematsu M, Gotoh N, Yoshida K. Novel navigation surgery using image fusion of PET/CT and sonography for axillary neoplasm: first experience. Int J Surg Case Rep. 2013;4:719–22.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Nakano S, Kousaka J, Fujii K, Yorozuya K, Yoshida M, Mouri Y, et al. Impact of real-time virtual sonography, a coordinated sonography and MRI system that uses an image fusion technique, on the sonographic evaluation of MRI-detected lesions of the breast in second-look sonography. Breast Cancer Res Treat. 2012;134:1179–88.CrossRefPubMedGoogle Scholar
  10. 10.
    Nakano S, Yoshida M, Fujii K, Yorozuya K, Kousaka J, Mouri Y, et al. Real-time virtual sonography, a coordinated sonography and MRI system that uses magnetic navigation, improves the sonographic identification of enhancing lesions on breast MRI. Ultrasound Med Biol. 2012;38:42–9.CrossRefPubMedGoogle Scholar
  11. 11.
    Nakano S, Ando T, Tetsuka R, Fujii K, Yoshida M, Kousaka J, et al. Reproducible surveillance breast ultrasound using an image fusion technique in a short-interval follow-up for BI-RADS 3 lesions: a pilot study. Ultrasound Med Biol. 2014;40:1049–57.CrossRefPubMedGoogle Scholar
  12. 12.
    Rizzatto G, Fausto A. Breast imaging and volume navigation: MR imaging and ultrasound coregistration. Ultrasound Clin. 2009;4:261–71.CrossRefGoogle Scholar
  13. 13.
    Uematsu T. Real-time virtual sonography (RVS)-guided vacuum-assisted breast biopsy for lesions initially detected with breast MRI. Jpn J Radiol. 2013;31:826–31.CrossRefPubMedGoogle Scholar
  14. 14.
    Yamamoto S, Maeda N, Tamesa M, Nagashima Y, Suga K, Oka M. Sentinel lymph node detection in breast cancer patients by real-time virtual sonography constructed with three-dimensional computed tomography-lymphography. Breast J. 2010;16:4–8.CrossRefPubMedGoogle Scholar
  15. 15.
    Yamamoto S, Maeda N, Tamesa M, Nagashima Y, Yoshimura K, Oka M. Prospective ultrasonographic prediction of sentinel lymph node metastasis by real-time virtual sonography constructed with three-dimensional computed tomography-lymphography in breast cancer patients. Breast Cancer. 2012;19:77–82.CrossRefPubMedGoogle Scholar
  16. 16.
    Yamamoto S, Maeda N, Yoshimura K, Oka M. Intraoperative detection of sentinel lymph nodes in breast cancer patients using ultrasonography-guided direct indocyanine green dye-marking by real-time virtual sonography constructed with three-dimensional computed tomography-lymphography. Breast. 2013;22:933–7.CrossRefPubMedGoogle Scholar
  17. 17.
    D’Orsi C, Mendelson E, Ikeda D. Breast imaging reporting and data system: ACR BIRADS—breast imaging Atlas. Reston: American College of Radiology; 2003.Google Scholar
  18. 18.
    Moyle P, Sonoda L, Britton P, Sinnatamby R. Incidental breast lesions detected on CT: what is their significance? Br J Radiol. 2010;83:233–40.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Porter G, Steel J, Paisley K, Watkins R, Holgate C. Incidental breast masses detected by computed tomography: are any imaging features predictive of malignancy? Clin Radiol. 2009;64:529–33.CrossRefPubMedGoogle Scholar
  20. 20.
    Tohnosu N, Okuyama K, Koide Y, Kikuchi T, Awano T, Matsubara H, et al. A comparison between ultrasonography and mammography, computed tomography and digital subtraction angiography for the detection of breast cancers. Surg Today. 1993;23:704–10.CrossRefPubMedGoogle Scholar
  21. 21.
    Henson DE, Tarone RE. Involution and the etiology of breast cancer. Cancer. 1994;74:424–9.CrossRefPubMedGoogle Scholar
  22. 22.
    Harish MG, Konda SD, MacMahon H, Newstead GM. Breast lesions incidentally detected with CT: what the general radiologist needs to know. Radiographics. 2007;27(Suppl 1):S37–51.CrossRefPubMedGoogle Scholar
  23. 23.
    Inoue M, Sano T, Watai R, Ashikaga R, Ueda K, Watatani M, et al. Dynamic multidetector CT of breast tumors: diagnostic features and comparison with conventional techniques. AJR Am J Roentgenol. 2003;181:679–86.CrossRefPubMedGoogle Scholar
  24. 24.
    Yi JG, Kim SJ, Marom EM, Park JH, Jung SI, Lee MW. Chest CT of incidental breast lesions. J Thorac Imaging. 2008;23:148–55.CrossRefPubMedGoogle Scholar
  25. 25.
    Satake H, Ishigaki S, Kitano M, Naganawa S. Prediction of prone-to-supine tumor displacement in the breast using patient position change: investigation with prone MRI and supine CT. Breast Cancer. 2014: (Epub ahead of print).Google Scholar
  26. 26.
    Ewertsen C, Saftoiu A, Gruionu LG, Karstrup S, Nielsen MB. Real-time image fusion involving diagnostic ultrasound. AJR Am J Roentgenol. 2013;200:W249–55.CrossRefPubMedGoogle Scholar
  27. 27.
    Akashi-Tanaka S, Sato N, Ohsumi S, Kimijima I, Inaji H, Teramoto S, et al. Evaluation of the usefulness of breast CT imaging in delineating tumor extent and guiding surgical management: a prospective multi-institutional study. Ann Surg. 2012;256:157–62.CrossRefPubMedGoogle Scholar

Copyright information

© The Japanese Breast Cancer Society 2014

Authors and Affiliations

  • Junko Kousaka
    • 1
  • Shogo Nakano
    • 1
    Email author
  • Takahito Ando
    • 1
  • Rie Tetsuka
    • 1
  • Kimihito Fujii
    • 1
  • Miwa Yoshida
    • 1
  • Yukako Shiomi-Mouri
    • 1
  • Manami Goto
    • 1
  • Yuko Imai
    • 1
  • Tsuneo Imai
    • 1
  • Takashi Fukutomi
    • 4
  • Eisuke Katsuda
    • 2
  • Tsuneo Ishiguchi
    • 2
  • Osamu Arai
    • 3
  1. 1.Division of Breast and Endocrine Surgery, Department of SurgeryAichi Medical UniversityNagakuteJapan
  2. 2.Department of RadiologyAichi Medical UniversityNagakuteJapan
  3. 3.Medical Systems Engineering Division 2, R&D Section 2 Engineering, R&D Department 1Hitachi Aloka Medical LtdKokubunjiJapan
  4. 4.Department of Breast SurgeryTokyo Saiseikai Central HospitalMinatokuJapan

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