Academic Radiology
Volume 15, Issue 11 , Pages 1437-1445 , November 2008

Evaluation of Computer-aided Diagnosis on a Large Clinical Full-field Digital Mammographic Dataset

  • Hui Li, PhD

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • Corresponding Author InformationAddress correspondence to: H.L.
    • ,
  • Maryellen L. Giger, PhD

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Yading Yuan, BS

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Weijie Chen, PhD

      Affiliations

    • Laboratory for the Assessment of Medical Imaging Systems, Division of Imaging and Applied Mathematics, Office of Science and Engineering Labs, CDRH, FDA, Silver Spring, MD
    • ,
  • Karla Horsch, PhD

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Li Lan, MS

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Andrew R. Jamieson, BS

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Charlene A. Sennett, MD

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637
    • ,
  • Sanaz A. Jansen, MS

      Affiliations

    • Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637

Received 15 February 2008 ,Accepted 11 March 2008.

References 

  1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2007. CA Cancer J Clin. 2007;57:43–66
  2. Chlebowski RT. Breast cancer risk reduction strategies for women at increased risk. Ann Rev Med. 2002;53:519–540
  3. Boyd NF, Lockwood GA, Martin LJ, et al. Mammographic densities and breast cancer risk. Breast Dis. 1998;10:113–126
  4. Kerlikowske K, Grady D, Rubin SM, et al. Efficacy of screening mammography: a meta-analysis. JAMA. 1995;273:149–154
  5. Hendrick RE, Smith RA, Rutledge JH, et al. Benefit of screening mammography in women aged 40–49: a new meta-analysis of randomized controlled trials. J Natl Cancer Inst Monogr. 1997;22:87–92
  6. Ciatto S, Rosselli DTM, Burke P, et al. Comparison of standard and double reading and computer-aided detection (CAD) of interval cancers at prior negative screening mammograms: blind review. Br J Cancer. 2003;89:1645–1649
  7. Nishikawa RM. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph. 2007;31:224–235
  8. Giger ML. Computerized analysis of images in the detection and diagnosis of breast cancer. Semin Ultrasound CT MRI. 2004;25:411–418
  9. Huo Z, Giger ML, Vyborny CJ, et al. Analysis of speculation in the computerized classification of mammographic masses. Med Phys. 1995;22:1569–1579
  10. Hadjiiski L, Sahiner B, Helvie MA, et al. Breast masses: computer-aided diagnosis with serial mammograms. Radiology. 2006;240:343–356
  11. Lo JY, Baker JA, Kornguth PJ, et al. Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. Acad Radiol. 1995;2:841–850
  12. Zheng B, Chang YH, Gur D. Adaptive computer-aided diagnosis scheme of digitized mammograms. Acad Radiol. 1996;3:806–814
  13. Veldkamp WJH, Karssemeijer N, Otten JDM, et al. Automated classification of clustered microcalcifications into malignant and benign types. Med Phys. 2000;27:2600–2608
  14. MQSA and Accreditation for Full-Field Digital MammographyAmerican College of Radiology. http://www.acr.org/accreditation/mammography/rsna07presentation.aspxAccessed June 4, 2008
  15. Giger ML, Huo Z, Kupinski MA, et al. Computer-aided diagnosis in mammography. In:  Sonka M,  Fitzpatrick MJ editor. Handbook of medical imaging. Bellingham, WA: SPIE; 2000;p. 915–1004
  16. Huo Z, Giger ML, Vyborny CJ, et al. Automated computerized classification of malignant and benign masses on digitized mammograms. Acad Radiol. 1998;5:155–168
  17. Huo Z, Giger ML, Metz CE. Effect of dominant features on neural network performance in the classification of mammographic lesions. Phys Med Biol. 1999;44:2579–2595
  18. Huo Z, Giger ML, Vyborny CJ, et al. Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. Acad Radiol. 2000;7:1077–1084
  19. Huo Z, Giger ML, Vyborny CJ. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis. IEEE Trans Med Imaging. 2001;20:1285–1292
  20. Horsch K, Giger ML, Vyborny CJ, et al. Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology. 2006;240:357–368
  21. Kupinski MA, Giger ML. Automated seeded lesion segmentation on digital mammograms. IEEE Trans Med Imaging. 1998;17:510–517
  22. Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Res Med. 2007;58:562–571
  23. Bishop C. Neural network for pattern recognition. Oxford: Oxford University Press; 1995;
  24. Huberty CJ. Applied discriminant analysis. New York: Wiley; 1994;
  25. Lachenbruch PL. Discriminant analysis. London, UK: Hafner; 1975;
  26. Metz CE. ROC methodology in radiographic imaging. Invest Radiol. 1986;21:720–733
  27. Metz CE. Some practical issues of experimental design and data analysis in radiological ROC studies. Invest Radiol. 1989;24:234–245
  28. Metz CE, Herman BA, Shen J. Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously distributed data. Stat Med. 1998;17:1033–1053
  29. Pesce LL, Metz CE. Reliable and computationally efficient maximum-likelihood estimation of “proper” binormal ROC curves. Acad Radiol. 2007;14:814–829
  30. Lachenbruch PA, Mickey MR. Estimation of error rates in discriminant analysis. Technometrics. 1968;10:1–11
  31. ROC software. University of Chicago website. http://www-radiology.uchicago.edu/krl/roc_soft6.htmAccessed February 23, 2007
  32. Li H, Giger ML, Yuan Y, et al. Comparison of computerized image analyses for digitized screen-film mammograms and full-field digital mammography images. Digital Mammography IWDM. 2006;569–575
  33. Li L, Clark RA, Thomas JA. Computer-aided diagnosis of masses with full-field digital mammography. Acad Radiol. 2002;9:4–12
  34. Ge J, Sahiner B, Hadjiiski LM, et al. Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med Phys. 2006;33:2975–2988
  35. Rana RS, Jiang Y, Schmidt RA, et al. Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms. Acad Radiol. 2007;14:363–370
  36. Sun X, Qian W, Song X, et al. Computer-aided detection (CAD) of breast cancer on full field digital and screening film mammograms. Proc SPIE. 2003;5032:930–939
  37. Wei J, Hadjiiski LM, Sahiner B, et al. Computer-aided detection system for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms. Acad Radiol. 2007;14:659–669
  38. Pisano E, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353:1773–1783
  39. Cole E, Pisano ED, Brown M, et al. Diagnostic accuracy of Fischer SenoScan digital mammography versus screen-film mammography in a diagnostic mammography population. Acad Radiol. 2004;11:879–886
  40. Seo K, Pisano ED, Kuzmiak CM, et al. The positive predictive value for diagnosis of breast cancer: full-field digital mammography versus film-screen mammography in the diagnostic mammographic population. Acad Radiol. 2006;13:1229–1235
  41. Lewin JM, D'Orsi CJ, Hendrick RE, et al. Clinical comparison of full-field digital mammography and screen-film mammography for detection of breast cancer. AJR Am J Roentgenol. 2002;179:671–677
  42. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65–70

1 This work was supported in part by USPHS Grants R01-CA89452, R21-CA113800, and P50-CA125183. M.L.G. is a shareholder in R2/Hologic, Inc. (Sunnyvale, CA). It is the policy of the University of Chicago that investigators disclose publicly actual or potential significant financial interests that may appear to be affected by the research activities.

PII: S1076-6332(08)00280-8

doi: 10.1016/j.acra.2008.05.004

Academic Radiology
Volume 15, Issue 11 , Pages 1437-1445 , November 2008

Article Tools

* Image Usage & Resolution