Skip to main content
SpringerLink
Log in

Quantitative Ultrasound of Tumor Surrounding Tissue for Enhancement of Breast Cancer Diagnosis

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10814))

Included in the following conference series:

Abstract

Breast cancer is one of the leading causes of cancer-related death in female patients. The quantitative ultrasound techniques being developed recently provide useful information facilitating the classification of tumors as malignant or benign. Quantitative parameters are typically determined on the basis of signals scattered within the tumor. The present paper demonstrates the utility of quantitative data estimated based on signal backscatter in the tissue surrounding the tumor. Two quantitative parameters, weighted entropy and Nakagami shape parameter were calculated from the backscatter signal envelope. The ROC curves and the AUC parameter values were used to assess their ability to classify neoplastic lesions. Results indicate that data from tissue surrounding the tumor may characterize it better than data from within the tumor. AUC values were on average 18% higher for parameters calculated from data collected from the tissue surrounding the lesion than from the data from the lesion itself.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D., Bray, F.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)

    Article  Google Scholar 

  2. Wojciechowska, U., Olasek, P., Czauderna, K., Didkowska, J.: Cancer in Poland in 2014. Centrum Onkologii-Instytut im, Marii Skłodowskiej-Curie (2016)

    Google Scholar 

  3. Kolb, T.M., Lichy, J., Newhouse, J.H.: Comparison of the performance of screening mammography, physical examination, and breast us and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 225(1), 165–175 (2002)

    Article  Google Scholar 

  4. Mandelson, M.T., Oestreicher, N., Porter, P.L., White, D., Finder, C.A., Taplin, S.H., White, E.: Breast density as a predictor of mammographic detection: comparison of interval-and screen-detected cancers. J. Natl Cancer Inst. 92(13), 1081–1087 (2000)

    Article  Google Scholar 

  5. Mendelson, E., Böhm-Vélez, M., Berg, W., Whitman, G., Feldman, M., Madjar, H., Rizzsatto, G., Baker, J., Zuley, M., Stavros, A., Comstock, C., Van Duyn Wear, V.: ACR BI-RADS® ultrasound. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System, vol. 149. American College of Radiology, Reston (2013)

    Google Scholar 

  6. D’Astous, F.T., Foster, F.S.: Frequency dependence of ultrasound attenuation and backscatter in breast tissue. Ultrasound Med. Biol. 12(10), 795–808 (1986)

    Article  Google Scholar 

  7. Nam, K., Zagzebski, J.A., Hall, T.J.: Quantitative assessment of in vivo breast masses using ultrasound attenuation and backscatter. Ultrason. Imaging 35(2), 146–161 (2013)

    Article  Google Scholar 

  8. Lizzi, F.L., Astor, M., Liu, T., Deng, C., Coleman, D.J., Silverman, R.H.: Ultrasonic spectrum analysis for tissue assays and therapy evaluation. Int. J. Imaging Syst. Technol. 8(1), 3–10 (1997)

    Article  Google Scholar 

  9. Moon, W.K., Lo, C.M., Chang, J.M., Huang, C.S., Chen, J.H., Chang, R.F.: Quantitative ultrasound analysis for classification of bi-rads category 3 breast masses. J. Digit. Imaging 26(6), 1091–1098 (2013)

    Article  Google Scholar 

  10. Tadayyon, H., Sadeghi-Naini, A., Czarnota, G.J.: Noninvasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images. Transl. Oncol. 7(6), 759–767 (2014)

    Article  Google Scholar 

  11. Cai, L., Wang, X., Wang, Y., Guo, Y., Yu, J., Wang, Y.: Robust phase-based texture descriptor for classification of breast ultrasound images. Biomed. Eng. Online 14(1), 26 (2015)

    Article  Google Scholar 

  12. Cheng, H.D., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn. 43(1), 299–317 (2010)

    Article  MATH  Google Scholar 

  13. Dutt, V., Greenleaf, J.F.: Ultrasound echo envelope analysis using a homodyned K distribution signal model. Ultrason. Imaging 16(4), 265–287 (1994)

    Article  Google Scholar 

  14. Hruska, D.P., Oelze, M.L.: Improved parameter estimates based on the homodyned K distribution. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56(11), 2471–2481 (2009)

    Article  Google Scholar 

  15. Hruska, D.P.: Improved techniques for statistical analysis of the envelope of backscattered ultrasound using the homodyned K distribution. Master’s thesis, University of Illinois at Urbana-Champaign (2009)

    Google Scholar 

  16. Trop, I., Destrempes, F., El Khoury, M., Robidoux, A., Gaboury, L., Allard, L., Chayer, B., Cloutier, G.: The added value of statistical modeling of backscatter properties in the management of breast lesions at us. Radiology 275(3), 666–674 (2014)

    Article  Google Scholar 

  17. Byra, M., Nowicki, A., Wróblewska-Piotrzkowska, H., Dobruch-Sobczak, K.: Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters. Med. Phys. 43(10), 5561–5569 (2016)

    Article  Google Scholar 

  18. Nakagami, M.: The m-distribution-a general formula of intensity distribution of rapid fading. In: Statistical Method of Radio Propagation (1960)

    Google Scholar 

  19. Shankar, P.M., Dumane, V.A., Reid, J.M., Genis, V., Forsberg, F., Piccoli, C.W., Goldberg, B.B.: Classification of ultrasonic B-mode images of breast masses using nakagami distribution. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 48(2), 569–580 (2001)

    Article  Google Scholar 

  20. Gefen, S., Tretiak, O.J., Piccoli, C.W., Donohue, K.D., Petropulu, A.P., Shankar, P.M., Dumane, V.A., Huang, L., Kutay, M.A., Genis, V., et al.: ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis. IEEE Trans. Med. Imaging 22(2), 170–177 (2003)

    Article  Google Scholar 

  21. Tsui, P.H., Chang, C.C., Ho, M.C., Lee, Y.H., Chen, Y.S., Chang, C.C., Huang, N.E., Wu, Z.H., Chang, K.J.: Use of nakagami statistics and empirical mode decomposition for ultrasound tissue characterization by a nonfocused transducer. Ultrasound Med. Biol. 35(12), 2055–2068 (2009)

    Article  Google Scholar 

  22. Tsui, P.H., Yeh, C.K., Liao, Y.Y., Chang, C.C., Kuo, W.H., Chang, K.J., Chen, C.N.: Ultrasonic nakagami imaging: a strategy to visualize the scatterer properties of benign and malignant breast tumors. Ultrasound Med. Biol. 36(2), 209–217 (2010)

    Article  Google Scholar 

  23. Liao, Y.Y., Tsui, P.H., Li, C.H., Chang, K.J., Kuo, W.H., Chang, C.C., Yeh, C.K.: Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and nakagami-parameter images. Med. Phys. 38(4), 2198–2207 (2011)

    Article  Google Scholar 

  24. Ma, H.Y., Lin, Y.H., Wang, C.Y., Chen, C.N., Ho, M.C., Tsui, P.H.: Ultrasound window-modulated compounding nakagami imaging: resolution improvement and computational acceleration for liver characterization. Ultrasonics 70, 18–28 (2016)

    Article  Google Scholar 

  25. Tsui, P.H., Wan, Y.L.: Application of ultrasound nakagami imaging for the diagnosis of fatty liver. J. Med. Ultrasound 24(2), 47–49 (2016)

    Article  Google Scholar 

  26. Dobruch-Sobczak, K., Piotrzkowska-Wróblewska, H., Roszkowska-Purska, K., Nowicki, A., Jakubowski, W.: Usefulness of combined bi-rads analysis and nakagami statistics of ultrasound echoes in the diagnosis of breast lesions. Clin. Radiol. 72(4), 339-e7 (2017)

    Article  Google Scholar 

  27. Destrempes, F., Cloutier, G.: A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope. Ultrasound Med. Biol. 36(7), 1037–1051 (2010)

    Article  Google Scholar 

  28. Tsui, P.H.: Ultrasound detection of scatterer concentration by weighted entropy. Entropy 17(10), 6598–6616 (2015)

    Article  Google Scholar 

  29. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)

    Article  MathSciNet  Google Scholar 

  30. Tsui, P.H., Chen, C.K., Kuo, W.H., Chang, K.J., Fang, J., Ma, H.Y., Chou, D.: Small-window parametric imaging based on information entropy for ultrasound tissue characterization. Sci. Rep. 7, 41004 (2017)

    Article  Google Scholar 

  31. Zhang, L., Li, J., Xiao, Y., Cui, H., Du, G., Wang, Y., Li, Z., Wu, T., Li, X., Tian, J.: Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision. Sci. Rep. 5, 11085 (2015)

    Article  Google Scholar 

  32. Zhou, J., Zhan, W., Chang, C., Zhang, X., Jia, Y., Dong, Y., Zhou, C., Sun, J., Grant, E.G.: Breast lesions: evaluation with shear wave elastography, with special emphasis on the “stiff rim” sign. Radiology 272(1), 63–72 (2014)

    Article  Google Scholar 

  33. Jakubowski, W., Dobruch-Sobczak, K., Migda, B.: Standards of the polish ultrasound society-update. Sonomammography examination. J. Ultrason. 12(50), 245 (2012)

    Article  Google Scholar 

  34. Tsui, P.H., Ma, H.Y., Zhou, Z., Ho, M.C., Lee, Y.H.: Window-modulated compounding nakagami imaging for ultrasound tissue characterization. Ultrasonics 54(6), 1448–1459 (2014)

    Article  Google Scholar 

  35. Wang, Q.A.: Probability distribution and entropy as a measure of uncertainty. J. Phys. A: Math. Theor. 41(6), 065004 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  36. Hughes, M.S.: Analysis of digitized waveforms using shannon entropy. J. Acoust. Soc. Am. 93(2), 892–906 (1993)

    Article  Google Scholar 

  37. Guiaşu, S.: Weighted entropy. Rep. Math. Phys. 2(3), 165–179 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  38. Shankar, P.M.: A general statistical model for ultrasonic backscattering from tissues. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47(3), 727–736 (2000)

    Article  Google Scholar 

  39. Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

  40. Stavros, A.T.: Breast Ultrasound. Lippincott Williams & Wilkins, Philadelphia (2004)

    Google Scholar 

Download references

Acknowledgements

This study was supported by the National Science Centre, Poland, grants 2016/23/B/ST8/03391, 2016/21/N/ST7/03029 and 2014/13/B/ST7/01271. The project was implemented using the infrastructure of CePT, Operational Program “Innovative economy” for 2007–2013.

Author information

Authors and Affiliations

  1. Institute of Fundamental Technological Research, PAS, Pawińskiego 5b, 02-106, Warsaw, Poland

    Ziemowit Klimonda, Katarzyna Dobruch-Sobczak, Hanna Piotrzkowska-Wróblewska, Piotr Karwat & Jerzy Litniewski

  2. Maria Skłodowska-Curie Memorial Cancer Centre and Institute of Oncology, Warsaw, Poland

    Katarzyna Dobruch-Sobczak

Authors
  1. Ziemowit Klimonda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katarzyna Dobruch-Sobczak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanna Piotrzkowska-Wróblewska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Piotr Karwat
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jerzy Litniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ziemowit Klimonda .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Francisco Ortuño

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Klimonda, Z., Dobruch-Sobczak, K., Piotrzkowska-Wróblewska, H., Karwat, P., Litniewski, J. (2018). Quantitative Ultrasound of Tumor Surrounding Tissue for Enhancement of Breast Cancer Diagnosis. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78759-6_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78758-9

  • Online ISBN: 978-3-319-78759-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation