Skip to main content

Advertisement

Log in

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at three-dimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student’s t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p < 0.001) for classifying the breast tumors as benign or malignant. The A Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A Z values between the proposed method and conventional vascularity index method using z test was 0.04.

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

Access this article

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Gupta MK, Qin RY: Mechanism and its regulation of tumor-induced angiogenesis. World J Gastroenterol 9:1144–1155, 2003

    PubMed  CAS  Google Scholar 

  2. Stuhrmann M, Aronius R, Schietzel M: Tumor vascularity of breast lesions: potentials and limits of contrast-enhanced Doppler sonography. Am J Roentgenol 175:1585–1589, 2000

    Article  CAS  Google Scholar 

  3. Hsiao YH, Kuo SJ, Liang WM, Huang YL, Chen DR: Intra-tumor flow index can predict the malignant potential of breast tumor: dependent on age and volume. Ultrasound Med Biol 34:88–95, 2008

    Article  PubMed  Google Scholar 

  4. Sehgal CM, Arger PH, Rowling SE, Conant EF, Reynolds C, Patton JA: Quantitative vascularity of breast masses by Doppler imaging: regional variations and diagnostic implications. J Ultrasound Med 19:427–440, 2000

    PubMed  CAS  Google Scholar 

  5. Strano S, Gombos EC, Friedland O, Mozes M: Color Doppler imaging of fibroadenomas of the breast with histopathologic correlation. J Clin Ultrasound 32:317–322, 2004

    Article  PubMed  Google Scholar 

  6. Germer U, Tetzlaff A, Geipel A, Diedrich K, Gembruch U: Strong impact of estrogen environment on Doppler variables used for differentiation between benign and malignant breast lesions. Ultrasound Obstet Gynecol 19:380–385, 2002

    Article  PubMed  CAS  Google Scholar 

  7. Holcombe C, Pugh N, Lyons K, Douglasjones A, Mansel RE, Horgan K: Blood-flow in breast-cancer and fibroadenoma estimated by color Doppler ultrasonography. Br J Surg 82:787–788, 1995

    Article  PubMed  CAS  Google Scholar 

  8. Wu CH, Hsu MM, Chang YL, Hsieh FJ: Vascular pathology of malignant cervical lymphadenopathy: qualitative and quantitative assessment with power Doppler ultrasound. Cancer 83:1189–1196, 1998

    Article  PubMed  CAS  Google Scholar 

  9. Huang YL, Kuo SJ, Hsu CC, Tseng HS, Hsiao YH, Chen DR: Computer-aided diagnosis for breast tumors by using vascularization of 3-D power Doppler ultrasound. Ultrasound in Med & Biol 35:1607–1614, 2009

    Article  Google Scholar 

  10. Kettenbach J, Helbich TH, Huber A, Zuna I, Dock W: Computer-assisted quantitative assessment of power Doppler US: effects of microbubble contrast agent in the differentiation of breast tumors. Eur J Radiol 53:238–244, 2005

    Article  PubMed  Google Scholar 

  11. Chang RF, Huang SF, Moon WK, Lee YH, Chen DR: Solid breast masses: neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification. Radiology 243:56–62, 2007

    Article  PubMed  Google Scholar 

  12. Huang SF, Chang RF, Moon WK, Lee YH, Chen DR, Suri JS: Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images. IEEE Trans Med Imaging 27:320–330, 2008

    Article  PubMed  Google Scholar 

  13. Molinari F, Mantovani A, Deandrea M, Limone P, Garberoglio R, Suri JS: Characterization of single thyroid nodules by contrast-enhanced 3-D ultrasound. Ultrasound Med Biol 36:1616–1625, 2010

    Article  PubMed  Google Scholar 

  14. Schneider M, et al.: Use of intravital microscopy to study the microvascular behavior of microbubble-based ultrasound contrast agents. Microcirculation 19:245–259, 2012

    Article  PubMed  Google Scholar 

  15. Maheo K, et al.: Non-invasive quantification of tumor vascular architecture during docetaxel-chemotherapy. Breast Cancer Res Treat 134:1013–1025, 2012

    Article  PubMed  CAS  Google Scholar 

  16. LeCarpentier GL, et al.: Suspicious breast lesions: assessment of 3D Doppler US indexes for classification in a test population and fourfold cross-validation scheme. Radiology 249:463–470, 2008

    Article  PubMed  Google Scholar 

  17. Gokalp G, Topal U, Kizilkaya E: Power Doppler sonography: anything to add to BI-RADS US in solid breast masses? Eur J Radiol 70:77–85, 2009

    Article  PubMed  Google Scholar 

  18. Chang RF, Wu WJ, Moon WK, Chen DR: Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol 29:679–686, 2003

    Article  PubMed  Google Scholar 

  19. Chen WM, Chang RF, Moon WK, Chen DR: Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis. Ultrasound Med Biol 29:1027–1035, 2003

    Article  PubMed  Google Scholar 

  20. Drukker K, Giger ML, Horsch K, Kupinski MA, Vyborny CJ, Mendelson EB: Computerized lesion detection on breast ultrasound. Med Phys 29:1438–1446, 2002

    Article  PubMed  Google Scholar 

  21. Drukker K, Giger ML, Vyborny CJ, Mendelson EB: Computerized detection and classification of cancer on breast ultrasound. Acad Radiol 11:526–535, 2004

    Article  PubMed  Google Scholar 

  22. Horsch K, Giger ML, Venta LA, Vyborny CJ: Computerized diagnosis of breast lesions on ultrasound. Med Phys 29:157–164, 2002

    Article  PubMed  Google Scholar 

  23. Kuo WJ, Chang RF, Moon WK, Lee CC, Chen DR: Computer-aided diagnosis of breast tumors with different US systems. Acad Radiol 9:793–799, 2002

    Article  PubMed  Google Scholar 

  24. Suri JS, Kathuria C, Chang R-F, Molinari F, Fenster A: Advances in Diagnostic and Therapeutic Ultrasound Imaging. Norwood, MA: Artech House, 2008

    Google Scholar 

  25. Lee JS: Digital image smoothing and the sigma filter. Computer Vision Graphics and Image Processing 24:255–269, 1983

    Article  Google Scholar 

  26. Malladi R, Sethian JA, Vemuri BC: Shape modeling with front propagation—a level set approach. IEEE Trans Pattern Anal Mach Intell 17:158–175, 1995

    Article  Google Scholar 

  27. Gonzalez RC, Woods RE: Digital Image Processing. Englewood Cliffs: Prentice-Hall, 1992

    Google Scholar 

  28. Osher S, Sethian JA: Fronts propagating with curvature-dependent speed—algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79:12–49, 1988

    Article  Google Scholar 

  29. Wu MH, Tsai SJ, Pan HA, Hsiao KY, Chang FM: Three-dimensional power Doppler imaging of ovarian stromal blood flow in women with endometriosis undergoing in vitro fertilization. Ultrasound Obstet Gynecol 21:480–485, 2003

    Article  PubMed  Google Scholar 

  30. Jain AK: Fundamentals of Digital Image Processing. Englewood Cliffs: Prentice-Hall, 1989

    Google Scholar 

  31. Haykin S: Neural Networks: A Comprehensive Foundation. Upper Saddle River: Prentice-Hall, 1999

    Google Scholar 

  32. Lendasse A, Wertz V, Verleysen M: Model selection with cross-validations and bootstraps—application to time series prediction with RBFN models. Artificial Neural Networks and Neural Information Processing-Ican/Iconip 2003 2714:573–580, 2003

    Google Scholar 

  33. Schroeder RJ, et al.: d-galactose-based signal-enhanced color Doppler sonography of breast tumors and tumor-like lesions. Invest Radiol 34:109–115, 1999

    Article  PubMed  CAS  Google Scholar 

  34. Carson PL, et al.: 3-D color Doppler image quantification of breast masses. Ultrasound Med Biol 24:945–952, 1998

    Article  PubMed  CAS  Google Scholar 

  35. Shen WC, Chang RF, Moon WK, Chou YH, Huang CS: Breast ultrasound computer-aided diagnosis using BI-RADS features. Acad Radiol 14:928–939, 2007

    Article  PubMed  Google Scholar 

  36. Shen WC, Chang RF, Moon WK: Computer aided classification system for breast ultrasound based on breast imaging reporting and data system (BI-RADS). Ultrasound Med Biol 33:1688–1698, 2007

    Article  PubMed  Google Scholar 

  37. Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM: Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 254:680–690, 2010

    Article  PubMed  Google Scholar 

  38. Bahri S, Chen JH, Yu HJ, Kuzucan A, Nalcioglu O, Su MY: Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer? Ann Oncol 19:822–U822, 2008

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank the National Science Council (NSC 96-2221-E-002-268-MY3), Ministry of Economic Affairs (100-EC-17-A-19-S1-164), and Ministry of Education (AE-00-00-06) of the Republic of China for the financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruey-Feng Chang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, YH., Chen, JH., Chang, YC. et al. Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images. J Digit Imaging 26, 731–739 (2013). https://doi.org/10.1007/s10278-012-9556-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-012-9556-5

Keywords

Navigation