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

Academic Radiology

Volumn 15, Issue 11, 2008, Pages 1437-1445

  Li, Hui ^a   Giger, Maryellen L ^a   Yuan, Yading ^a   Chen, Weijie ^b   Horsch, Karla ^a   Lan, Li ^a   Jamieson, Andrew R ^a   Sennett, Charlene A ^a   Jansen, Sanaz A ^a  

^a UNIVERSITY OF CHICAGO   (United States)

^b Division of Imaging and Applied Mathematics   (United States)

Author keywords

breast mass classification; Computer aided diagnosis; full field digital mammography

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; BREAST LESION; BREAST TUMOR; CLINICAL EVALUATION; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DIGITAL MAMMOGRAPHY; HUMAN; IMAGE ANALYSIS; MAJOR CLINICAL STUDY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC;

AREA UNDER CURVE; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MAMMOGRAPHY; RADIOGRAPHIC IMAGE ENHANCEMENT; RETROSPECTIVE STUDIES;

EID: 55349095801     PISSN: 10766332     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.acra.2008.05.004     Document Type: Article

References (42)

1. Jemal A., Siegel R., Ward E., et al. Cancer statistics, 2007. CA Cancer J Clin 57 (2007) 43-66
2. Chlebowski R.T. Breast cancer risk reduction strategies for women at increased risk. Ann Rev Med 53 (2002) 519-540
3. Boyd N.F., Lockwood G.A., Martin L.J., et al. Mammographic densities and breast cancer risk. Breast Dis 10 (1998) 113-126
4. Kerlikowske K., Grady D., Rubin S.M., et al. Efficacy of screening mammography: a meta-analysis. JAMA 273 (1995) 149-154
5. Hendrick R.E., Smith R.A., Rutledge III J.H., et al. Benefit of screening mammography in women aged 40-49: a new meta-analysis of randomized controlled trials. J Natl Cancer Inst Monogr 22 (1997) 87-92
6. Ciatto S., Rosselli D.T.M., 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 89 (2003) 1645-1649
7. Nishikawa R.M. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph 31 (2007) 224-235
8. Giger M.L. Computerized analysis of images in the detection and diagnosis of breast cancer. Semin Ultrasound CT MRI 25 (2004) 411-418
9. Huo Z., Giger M.L., Vyborny C.J., et al. Analysis of speculation in the computerized classification of mammographic masses. Med Phys 22 (1995) 1569-1579
10. Hadjiiski L., Sahiner B., Helvie M.A., et al. Breast masses: computer-aided diagnosis with serial mammograms. Radiology 240 (2006) 343-356
11. Lo J.Y., Baker J.A., Kornguth P.J., et al. Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. Acad Radiol 2 (1995) 841-850
12. Zheng B., Chang Y.H., and Gur D. Adaptive computer-aided diagnosis scheme of digitized mammograms. Acad Radiol 3 (1996) 806-814
13. Veldkamp W.J.H., Karssemeijer N., Otten J.D.M., et al. Automated classification of clustered microcalcifications into malignant and benign types. Med Phys 27 (2000) 2600-2608
14. MQSA and Accreditation for Full-Field Digital Mammography, and American College of Radiology. http://www.acr.org/accreditation/mammography/rsna07presentation.aspx Accessed June 4, 2008
15. Giger M.L., Huo Z., Kupinski M.A., et al. Computer-aided diagnosis in mammography. In: Sonka M., and Fitzpatrick M.J. (Eds). Handbook of medical imaging (2000), SPIE, Bellingham, WA 915-1004
16. Huo Z., Giger M.L., Vyborny C.J., et al. Automated computerized classification of malignant and benign masses on digitized mammograms. Acad Radiol 5 (1998) 155-168
17. Huo Z., Giger M.L., and Metz C.E. Effect of dominant features on neural network performance in the classification of mammographic lesions. Phys Med Biol 44 (1999) 2579-2595
18. Huo Z., Giger M.L., Vyborny C.J., et al. Computerized classification of benign and malignant masses on digitized mammograms: a study of robustness. Acad Radiol 7 (2000) 1077-1084
19. Huo Z., Giger M.L., and Vyborny C.J. Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis. IEEE Trans Med Imaging 20 (2001) 1285-1292
20. Horsch K., Giger M.L., Vyborny C.J., et al. Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology 240 (2006) 357-368
21. Kupinski M.A., and Giger M.L. Automated seeded lesion segmentation on digital mammograms. IEEE Trans Med Imaging 17 (1998) 510-517
22. Chen W., Giger M.L., Li H., et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Res Med 58 (2007) 562-571
23. Bishop C. Neural network for pattern recognition (1995), Oxford University Press, Oxford
24. Huberty C.J. Applied discriminant analysis (1994), Wiley, New York
25. Lachenbruch P.L. Discriminant analysis (1975), Hafner, London, UK
26. Metz C.E. ROC methodology in radiographic imaging. Invest Radiol 21 (1986) 720-733
27. Metz C.E. Some practical issues of experimental design and data analysis in radiological ROC studies. Invest Radiol 24 (1989) 234-245
28. Metz C.E., Herman B.A., and Shen J. Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously distributed data. Stat Med 17 (1998) 1033-1053
29. Pesce L.L., and Metz C.E. Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves. Acad Radiol 14 (2007) 814-829
30. Lachenbruch P.A., and Mickey M.R. Estimation of error rates in discriminant analysis. Technometrics 10 (1968) 1-11
31. ROC software. University of Chicago website. http://www-radiology.uchicago.edu/krl/roc_soft6.htm Accessed February 23, 2007
32. Li H., Giger M.L., 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 R.A., and Thomas J.A. Computer-aided diagnosis of masses with full-field digital mammography. Acad Radiol 9 (2002) 4-12
34. Ge J., Sahiner B., Hadjiiski L.M., et al. Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med Phys 33 (2006) 2975-2988
35. Rana R.S., Jiang Y., Schmidt R.A., et al. Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms. Acad Radiol 14 (2007) 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 5032 (2003) 930-939
37. Wei J., Hadjiiski L.M., 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 14 (2007) 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 353 (2005) 1773-1783
39. Cole E., Pisano E.D., Brown M., et al. Diagnostic accuracy of Fischer SenoScan digital mammography versus screen-film mammography in a diagnostic mammography population. Acad Radiol 11 (2004) 879-886
40. Seo K., Pisano E.D., Kuzmiak C.M., 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 13 (2006) 1229-1235
41. Lewin J.M., D'Orsi C.J., Hendrick R.E., et al. Clinical comparison of full-field digital mammography and screen-film mammography for detection of breast cancer. AJR Am J Roentgenol 179 (2002) 671-677
42. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 6 (1979) 65-70