Skip to main content

Advertisement

Log in

Differentiation of benign and malignant breast lesions by mechanical imaging

  • Clinical trial
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Mechanical imaging yields tissue elasticity map and provides quantitative characterization of a detected pathology. The changes in the surface stress patterns as a function of applied load provide information about the elastic composition and geometry of the underlying tissue structures. The objective of this study is the clinical evaluation of breast mechanical imager for breast lesion characterization and differentiation between benign and malignant lesions. The breast mechanical imager includes a probe with pressure sensor array, an electronic unit providing data acquisition from the pressure sensors and communication with a touch-screen laptop computer. We have developed an examination procedure and algorithms to provide assessment of breast lesion features such as hardness related parameters, mobility, and shape. A statistical Bayesian classifier was constructed to distinguish between benign and malignant lesions by utilizing all the listed features as the input. Clinical results for 179 cases, collected at four different clinical sites, have demonstrated that the breast mechanical imager provides a reliable image formation of breast tissue abnormalities and calculation of lesion features. Malignant breast lesions (histologically confirmed) demonstrated increased hardness and strain hardening as well as decreased mobility and longer boundary length in comparison with benign lesions. Statistical analysis of differentiation capability for 147 benign and 32 malignant lesions revealed an average sensitivity of 91.4% and specificity of 86.8% with a standard deviation of ±6.1%. The area under the receiver operating characteristic curve characterizing benign and malignant lesion discrimination is 86.1% with the confidence interval ranging from 80.3 to 90.9%, with a significance level of P = 0.0001 (area = 50%). The multisite clinical study demonstrated the capability of mechanical imaging for characterization and differentiation of benign and malignant breast lesions. We hypothesize that the breast mechanical imager has the potential to be used as a cost effective device for cancer diagnostics that could reduce the benign biopsy rate, serve as an adjunct to mammography and to be utilized as a screening device for breast cancer detection.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Pisano ED, Gatsonis C, Hendrick E et al (2005) Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 353:1773–1783. doi:10.1056/NEJMoa052911

    Article  CAS  PubMed  Google Scholar 

  2. Skaane P, Hofvind S, Skjennald A (2007) Randomized trial of screen-film versus full-field digital mammography with soft-copy reading in population-based screening program: follow-up and final results of Oslo II study. Radiology 244:708–717. doi:10.1148/radiol.2443061478

    Article  PubMed  Google Scholar 

  3. American Cancer Society: breast cancer facts & Figs 2007–2008. American Cancer Society, Atlanta

  4. Altmann A, Hellerhoff K, Heywang-Köbrunner SH (2006) Screening in women with increased breast cancer risk. Breast Care 1:22–25. doi:10.1159/000091116

    Article  Google Scholar 

  5. Berg WA, Blume JD, Cormack JB et al (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163. doi:10.1001/jama.299.18.2151

    Article  CAS  PubMed  Google Scholar 

  6. Ely S, Vioral AN (2007) Breast cancer overview. Plast Surg Nurs 27:128–133

    PubMed  Google Scholar 

  7. Adhani AR, Ah-See ML, Makris A (2005) MRI in the detection and management of breast cancer. Expert Rev Anticancer Ther 239–252

  8. Vandermeer FQ, Bluemke DA (2007) Breast MRI: state of the art. Cancer Invest 25:384–392. doi:10.1080/07357900701360013

    Article  PubMed  Google Scholar 

  9. McDonald S, Saslow D, Alciati MN (2004) Performance and reporting of clinical breast examination: a review of the literature. CA Cancer J Clin 54:345–361. doi:10.3322/canjclin.54.6.345

    Article  PubMed  Google Scholar 

  10. Oestreicher N, White E, Lehman CD et al (2002) Predictors of sensitivity of clinical breast examination (CBE). Breast Cancer Res Treat 76:73–81. doi:10.1023/A:1020280623807

    Article  CAS  PubMed  Google Scholar 

  11. Bancej C, Decker K, Chiarelli A et al (2003) Contribution of clinical breast examination to mammography screening in the early detection of breast cancer. J Med Screen 10:16–21. doi:10.1258/096914103321610761

    Article  CAS  PubMed  Google Scholar 

  12. Saslow D, Hannan J, Osuch J et al (2004) Clinical breast examination: practical recommendations for optimizing performance and reporting. CA Cancer J Clin 54:327–344. doi:10.3322/canjclin.54.6.327

    Article  PubMed  Google Scholar 

  13. Barton MB, Harris R, Fletcher SW (1999) Does this patient have breast cancer? The screening clinical breast examination: should it be done? How? JAMA 282:1270–1280. doi:10.1001/jama.282.13.1270

    Article  CAS  PubMed  Google Scholar 

  14. Bobo JK, Lee NC, Thames SF (2000) Findings from 752, 081 clinical breast examinations reported to a national screening program from 1995 through 1998. J Natl Cancer Inst 92:971–976. doi:10.1093/jnci/92.12.971

    Article  CAS  PubMed  Google Scholar 

  15. Sarvazyan A (1998) Mechanical imaging: a new technology for medical diagnostics. Int J Med Inform 49:195–216. doi:10.1016/S1386-5056(98)00040-9

    Article  CAS  PubMed  Google Scholar 

  16. Egorov V, Sarvazyan A (2007) Breast tissue characterization by means of surface stress patterns analysis. Abstracts of 32nd International Symposium on Ultrasonic Imaging and Tissue Characterization, Arlington, p 45

  17. Egorov V, Sarvazyan AP (2008) Mechanical imaging of the breast. IEEE Trans Med Imaging 28(9):1275–1287. doi:10.1109/TMI.2008.922192

    Article  Google Scholar 

  18. Schnitt ST, Connolly JL (2004) Pathology breast disorders. In: Harris JR, Lippman ME, Morrow M, Osborne CK (eds) Diseases of the breast. Wolters Kluwer Company, Philadelphia, pp 77–99

    Google Scholar 

  19. Schnitt ST, Guidi AJ (2004) Pathology of invasive breast cancer. In: Harris JR, Lippman ME, Morrow M, Osborne CK (eds) Diseases of the breast. Wolters Kluwer Company, Philadelphia, pp 541–584

    Google Scholar 

  20. American Cancer Society Cancer Facts & Figures (2007) American Cancer Society, Atlanta

  21. Tukey JW (1977) Exploratory data analysis. Addison-Wesley Publishing, Princeton

    Google Scholar 

  22. McGill R, Tukey JW, Larsen WA (1978) Variations of box plots. Am Stat 32:12–16. doi:10.2307/2683468

    Article  Google Scholar 

  23. Gayen AK (1951) The frequency distribution of the product moment correlation coefficient in random samples of any size draw from non-normal universes. Biometrika 38:219–247

    CAS  PubMed  Google Scholar 

  24. Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 38:404–415. doi:10.1016/j.jbi.2005.02.008

    Article  PubMed  Google Scholar 

  25. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. doi:10.1126/science.3287615

    Article  CAS  PubMed  Google Scholar 

  26. Rish I (2001) An empirical study of the naive Bayes classifier. IBM Research Report RC 22230, Available online: http://www.cc.gatech.edu/~isbell/reading/papers/Rish.pdf Accessed 23 Sep 2008

  27. Kotsiantis S, Pintelas P (2005) Logitboost of simple Bayesian classifier. Computational intelligence in data mining. Spec Issue Informatica J 29(1):53–59

    Google Scholar 

  28. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103–130. doi:10.1023/A:1007413511361

    Article  Google Scholar 

  29. Sarvazyan AP (1998) Computerized palpation is more sensitive than human finger. In: Proceeding of 12th Int Symposium on Biomedical Measurements and Instrumentation. Dubrovnik-Croatia, pp 523–524

  30. Weiss RE, Hartanto V, Perrotti M et al (2001) In vitro trial of the pilot prototype of the prostate mechanical imaging system. Urology 58(6):1059–1063. doi:10.1016/S0090-4295(01)01407-8

    Article  CAS  PubMed  Google Scholar 

  31. Kearney TJ, Airapetian S, Sarvazyan AP (2004) Tactile breast imaging to increase the sensitivity of breast examination. J Clin Oncol 22S:103

    Google Scholar 

  32. Gathani T, Bull D, Green J, Reeves G, Beral V, the Million Women Study Collaborators (2005) Breast cancer histological classification: agreement between the Office for National Statistics and the National Health Service Breast Screening Programme. Breast Cancer Res 7:R1090–R1096. doi:10.1186/bcr1352

    Article  PubMed  Google Scholar 

  33. Sarvazyan AP (2001) Elastic properties of soft tissues. In: Levy M , Bass H, Stern R (eds) Handbook of elastic properties of solids, liquids and gases, vol 3. Academic Press, New York, pp 107–127 (Chap 5)

    Chapter  Google Scholar 

  34. Skovoroda AR, Klishko AN, Gusakyan DA et al (1995) Quantitative analysis of the mechanical characteristics of pathologically changed soft biological tissues. Biophysics 40:1359–1364

    Google Scholar 

  35. Krouskop TA, Wheeler TM, Kallel F, Garra BS, Hall T (1998) Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging 20:260–274

    CAS  PubMed  Google Scholar 

  36. Wellman P, Howe RH, Dalton E, Kern KA (1999) Breast tissue stiffness in compression is correlated to histological diagnosis. Technical Report, Harvard Biorobotics Laboratory, pp 1–15

    Google Scholar 

  37. Itoh A, Ueno E, Tohno E et al (2006) Breast disease: clinical application of US elastography for diagnosis. Radiology 9:341–350. doi:10.1148/radiol.2391041676

    Article  Google Scholar 

  38. Zhang XF, Liu XM, Bao XF et al (2006) Application of real-time tissue elastography in diagnosis of breast cancer. Zhejiang Da Xue Xue Bao Yi Xue Ban 35:444–447 (Article in Chinese)

    PubMed  Google Scholar 

  39. Thomas A, Fischer T, Frey H, Ohlinger R, Grunwald S, Blohmer JU et al (2006) Real-time elastography—an advanced method of ultrasound: first results in 108 patients with breast lesions. Ultrasound Obstet Gynecol 28:335–340. doi:10.1002/uog.2823

    Article  CAS  PubMed  Google Scholar 

  40. Sinkus R, Siegmann K, Tanter M, Xydeas T, Fink M (2006) MR–elastography is capable of increasing the specificity of MR-mammography—influence of rheology on the diagnostic gain. In: Proceedings of the 5th International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, Snowbird, p 111

  41. Regner DM, Hesley GK, Hangiandreou NJ et al (2006) Breast lesions: evaluation with US strain imaging—clinical experience of multiple observers. Radiology 238:425–437. doi:10.1148/radiol.2381041336

    Article  PubMed  Google Scholar 

  42. Barr RG, Grajo JR (2007) Initial results of real-time elasticity imaging in the evaluation of breast lesions. In: Proceedings of the 6th International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, Santa Fe, p 94

  43. Garra BS, Mobbs LM, Chant CM, Ophir J (2006) Clinical breast elastography: blinded reader performance and strategies for improving reader performance. In: Proceedings of the 5th International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, Snowbird, p 60

  44. Burnside ES, Hall TJ, Sommer AM et al (2007) Differentiating benign from malignant solid breast masses with US strain imaging. Radiology 245:401–410. doi:10.1148/radiol.2452061805

    Article  PubMed  Google Scholar 

  45. Svensson WE, Zaman N, Barrett NK, et al. (2007) Breast elasticity imaging aids patient management in the one stop breast clinic. In: Proceedings of the 6th International Conference on the Ultrasonic Measurement and Imaging of Tissue Elasticity, Santa Fe, p 128

  46. Kaufman CS, Jacobson L, Bachman B, Kaufman L (2006) Digital documentation of the physical examination: moving the clinical breast exam to the electronic medical record. Am J Surg 192:444–449. doi:10.1016/j.amjsurg.2006.06.006

    Article  PubMed  Google Scholar 

  47. Tanter M, Bercoff J, Athanasiou A et al (2008) Quantitative assessment of breast lesion viscoelasticity: initial clinical results using supersonic shear imaging. Ultrasound Med Biol 34(9):1373–1386. doi:10.1016/j.ultrasmedbio.2008.02.002

    Article  PubMed  Google Scholar 

  48. Sarvazyan A, Egorov V, Son JS, Kaufman CS (2008) Cost-effective screening for breast cancer worldwide: current state and future directions. Breast Cancer Basic Clin Res 1:91–99

    Google Scholar 

  49. Liang W, Lawrence W, Burnett CB et al (2003) Acceptability of diagnostic tests for breast cancer. Breast Cancer Res Treat 79:199–206. doi:10.1023/A:1023914612152

    Article  CAS  PubMed  Google Scholar 

  50. Gur D, Wallace LP, Klym AH et al (2005) Trends in recall, biopsy, and positive biopsy rates for screening mammography in an academic practice. Radiology 235:396–401. doi:10.1148/radiol.2352040422

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Ralph Tullo, MD, Breast Health Institute of Maitland, Florida, for his assistance in the clinical study. They also appreciate the engineering support of Milind Patel for the Breast Mechanical Imager. This work was supported by National Institute of Health under research grant CA091392 “Imaging Network for Breast Cancer Mass Screening”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vladimir Egorov.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Egorov, V., Kearney, T., Pollak, S.B. et al. Differentiation of benign and malignant breast lesions by mechanical imaging. Breast Cancer Res Treat 118, 67–80 (2009). https://doi.org/10.1007/s10549-009-0369-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-009-0369-2

Keywords

Navigation