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Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions

  • Conference paper
Digital Mammography (IWDM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4046))

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Abstract

The objective of the study was to assess the potential of a CAD device with computer aided classification capabilities to reduce interventional procedures for BI-RADS category 4A lesions. 113 such lesions (17 masses, 96 clusters), forwarded for biopsy (103 benign) were analyzed retrospectively by a CAD device that generated descriptors. The device extracted quantitative features characterizing the lesions by shape, margins, size and distribution. Descriptors taken from the BI-RADS lexicon for the appearance of the lesion were generated based on the values of the quantitative features. A paradigm based on the computer generated descriptors was developed to assist in assigning a level of suspicion. The paradigm deemed malignant, all 10 malignant cases of the study (100% sensitivity) and correctly classified 38 of the 103 benign lesions. The CAD-generated descriptors, thus, eliminated 36.9% of unnecessary biopsies without decreasing the sensitivity.

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References

  1. Warren-Burhenne, L.L., Wood, S.A., D’Orsi, C.A., Feig, S.A., Kopans, D.B., O’Shaughnessy, K.F., Sickles, E.A., Tabar, L., Vyborny, C.J., Castellino, R.A.: Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215, 554–562 (2000)

    Google Scholar 

  2. Ikeda, D.M., Birdwell, R.L., O’Shaughnessy, K.F., Sickles, E.A., Brenner, R.J.: Computer-aided detection output on 172 subtle findings on normal mammograms previously obtained in women with breast cancer detected at follow-up screening mammography. Radiology 230, 811–819 (2004)

    Article  Google Scholar 

  3. Roque, A.C., Andre, T.C.: Mammography and computerized decision systems: a review. Ann. NY Acad. Sci. 980, 83–94 (2002)

    Article  Google Scholar 

  4. Gur, D., Sumkin, J.H., Rockette, H.E., Ganott, M., Hakim, C., Hardesty, L., Poller, W.R., Shah, R., Wallace, L.: Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J. Natl. Cancer Inst. 96, 185–190 (2004)

    Article  Google Scholar 

  5. Doi, K., MacMahon, H., Katswragawa, S., Nishikawa, R.M., Jiang, W.: Computer-aided diagnosis in radiology: potential and pitfalls. Eur. J. Radiol. 31, 97–109 (1999)

    Article  Google Scholar 

  6. Lederman, R., Leichter, I., Novak, B., Bamberger, P., Fields, S., Buchbinder, S.: Stratification of mammographic cases by BIRADS category with the assistance of a CAD system. European Radiology 13, 347–353 (2003)

    Google Scholar 

  7. Sickles, E.A.: Mammographic features of “early” breast cancer. Am. J. Roengenol 143, 461–464 (1984)

    Google Scholar 

  8. Kopans, D.B.: The positive predictive value of mammography. Am. J. Roengenol 158, 521–526 (1992)

    Google Scholar 

  9. Fu, J.C., Lee, S.K., Wong, S.T., Yeh, J.Y., Wang, A.H., Wu, H.K.: Image segmentation feature selection and pattern classification for mammographic microcalcifications. Comput. Med. Imaging Graph 9, 419–429 (2005)

    Google Scholar 

  10. Papadopoulos, A., Fotiadis, D.I., Likas, A.: Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artif. Intell. Med. 34, 141–150 (2005)

    Article  Google Scholar 

  11. Wei, L., Yang, Y., Nishikawa, R.M., Jiang, Y.: A Study on Several Machine-Learning Methods for Classification of Malignant and Benign microcalcifications. IEEE Trans. Med. Imaging 24, 371–380 (2005)

    Article  Google Scholar 

  12. Burnside, E.S., Sisney, G.A., Rubin, D.L., Ochsner, J.E., Fowler, K.: The Ability of Microcalcification Descriptors in the BI-RADS, To Stratify the Risk of Malignancy. In: Proc. of 91th RSNA, Chicago, USA, 4th edn. November 27 (2005)

    Google Scholar 

  13. Fields, S., Lederman, R., Buchbinder, S., Novak, B., Sklair, M., Bamberger, P., Leichter, I.: Improved mammographic accuracy with CAD assisted ranking of lesions. In: Proc. of the 6th IWDM, Bremen, Germany (June 2002)

    Google Scholar 

  14. Fields, S., Leichter, I., Lederman, R., Buchbinder, S., Novak, B., Sklair-Levy, M., Sperber, F., Bamberger, P.: Improving the Performance of Mammographic Assessment by the use of an Advanced Two- tiered CAD/CAC System. In: Proc. of the 7th IWDM, Charlotte, NC, U.S.A (June 2004)

    Google Scholar 

  15. Leichter, I., Lederman, R., Bamberger, P., Novak, B., Fields, S., Buchbinder, S.: The use of an interactive software program for quantitative characterization of microcalcifications on digitized film-screen mammograms. Invest. Radiol. 34, 394–400 (1999)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Isaac, L., Richard, L., Shalom, B., Yossi, S., Philippe, B., Fanny, S. (2006). Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_11

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  • DOI: https://doi.org/10.1007/11783237_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35625-7

  • Online ISBN: 978-3-540-35627-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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