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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 832509, 10 pages
Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Dr., Atlanta, GA 30332, USA
2Department of Radiology, University of Wisconsin Medical School, E3/311, 600 Highland Avenue, Madison, WI 53792-3252, USA
Received 18 January 2013; Accepted 22 April 2013
Academic Editor: Yi-Hong Chou
Copyright © 2013 Turgay Ayer et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- S. H. Parker, F. Burbank, R. J. Jackman et al., “Percutaneous large-core breast biopsy: a multi-institutional study,” Radiology, vol. 193, no. 2, pp. 359–364, 1994.
- L. M. Wun, R. M. Merrill, and E. J. Feuer, “Estimating lifetime and age-conditional probabilities of developing cancer,” Lifetime Data Analysis, vol. 4, no. 2, pp. 169–186, 1998.
- American Cancer Society, Breast Cancer Facts & Figures 2011-2012, American Cancer Society, Atlanta, Ga, USA, 2011.
- H. C. Zuckerman, “The role of mammography in the diagnosis of breast cancer,” Breast Cancer—Diagnosis and Treatment, pp. 152–172, 1987.
- R. A. Smith, D. Saslow, K. A. Sawyer et al., “American cancer society guidelines for breast cancer screening: update 2003,” CA: A Cancer Journal for Clinicians, vol. 53, no. 3, pp. 141–169, 2003.
- National Center for Health Statistics Health (NCHS), United States, 2005 with Chartbook on Trends in the Health of Americans Hyattsville, National Center for Health Statistics Health, Hyattsville, Md, USA, 2005.
- Census.gov, Basic Counts/Population, 2005, http://factfinder.census.gov/servlet/ACSSAFFPeople?_submenuId=people_0&_sse=on.
- M. L. Brown, F. Houn, E. A. Sickles, and L. G. Kessler, “Screening mammography in community practice: positive predictive value of abnormal findings and yield of follow-up diagnostic procedures,” The American Journal of Roentgenology, vol. 165, no. 6, pp. 1373–1377, 1995.
- Breastcancer.org, Biopsy, 2006, http://www.breastcancer.org/testing_biopsy.html.
- E. A. Sickles, D. E. Wolverton, and K. E. Dee, “Performance parameters for screening and diagnostic mammography: specialist and general radiologists,” Radiology, vol. 224, no. 3, pp. 861–869, 2002.
- R. Smith-Bindman, P. W. Chu, D. L. Miglioretti et al., “Comparison of screening mammography in the United States and the United Kingdom,” The Journal of the American Medical Association, vol. 290, no. 16, pp. 2129–2137, 2003.
- American College of Radiology, Breast Imaging Reporting and Data System (BIRADS), American College of Radiology, Reston, Va, USA, 4th edition, 2003.
- M. L. Giger, “Computer-aided diagnosis in radiology,” Academic Radiology, vol. 9, no. 1, pp. 1–3, 2002.
- D. Kahneman, P. Slovic, and A. Tversky, Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge, UK, 2001.
- J. G. Elmore, C. K. Wells, C. H. Lee, D. H. Howard, and A. R. Feinstein, “Variability in radiologists' interpretations of mammograms,” The New England Journal of Medicine, vol. 331, no. 22, pp. 1493–1499, 1994.
- E. C. Y. Chan, “Promoting an ethical approach to unproven screening imaging tests,” Journal of the American College of Radiology, vol. 2, no. 4, pp. 311–320, 2005.
- B. J. Hillman, “Informed and shared decision making: an alternative to the debate over unproven screening tests,” Journal of the American College of Radiology, vol. 2, no. 4, pp. 297–298, 2005.
- E. Picano, “Informed consent and communication of risk from radiological and nuclear medicine examinations: how to escape from a communication inferno,” The British Medical Journal, vol. 329, no. 7470, pp. 849–851, 2004.
- L. Hadjiiski, B. Sahiner, M. A. Helvie et al., “Breast masses: computer-aided diagnosis with serial mammograms,” Radiology, vol. 240, no. 2, pp. 343–356, 2006.
- H. P. Chan, B. Sahiner, M. A. Helvie et al., “Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study,” Radiology, vol. 212, no. 3, pp. 817–827, 1999.
- Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz, “Breast cancer: effectiveness of computer-aided diagnosis—observer study with independent database of mammograms,” Radiology, vol. 224, no. 2, pp. 560–568, 2002.
- M. Kallergi, “Computer-aided diagnosis of mammographic microcalcification clusters,” Medical Physics, vol. 31, no. 2, pp. 314–326, 2004.
- Y. Jiang, C. E. Metz, R. M. Nishikawa, and R. A. Schmidt, “Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications,” Academic Radiology, vol. 13, no. 1, pp. 84–94, 2006.
- M. Giger, Z. Huo, and M. Kupinski, “Computer-aided diagnosis in mammography,” in Handbook of Medical Imaging, vol. 2, pp. 917–986, SPIE, Washington, DC, USA, 2000.
- T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn, R. W. Woods, and E. S. Burnside, “Comparison of logistic regression and artificial neural network models in breast cancer risk estimation,” Radiographics, vol. 30, no. 1, pp. 13–22, 2010.
- J. E. Dayhoff and J. M. DeLeo, “Artificial neural networks: opening the black box,” Cancer, vol. 91, no. 8, supplement, pp. 1615–1635, 2001.
- M. K. Markey, G. D. Tourassi, M. Margolis, and D. M. DeLong, “Impact of missing data in evaluating artificial neural networks trained on complete data,” Computers in Biology and Medicine, vol. 36, no. 5, pp. 516–525, 2006.
- J. Lawrence, Introduction to Neural Networks, California Scientific Software, Nevada City, Calif, USA, 1993.
- A. J. Maren, C. T. Harston, and R. M. Pap, Handbook of Neural Computing Applications, edited by A. J. Maren, C. T. Harston, R. M. Pap, Academic Press, San Diego, Calif, USA, 1990.
- W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R. A. Schmidt, “Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network,” Medical Physics, vol. 21, no. 4, pp. 517–524, 1994.
- M. P. Sampat, M. K. Markey, and A. C. Bovik, “Computer-aided detection and diagnosis in mammography,” in Handbook of Image and Video Processing, vol. 2, pp. 1195–1217, 2005.
- K. Kerlikowske, P. A. Carney, B. Geller et al., “Performance of screening mammography among women with and without a first-degree relative with breast cancer,” Annals of Internal Medicine, vol. 133, no. 11, pp. 855–863, 2000.
- R. G. Stafford, J. Beutel, D. J. Mickewich, and S. L. Albers, “Application of neural networks to computer-aided pathology detection in mammography,” in Medical Imaging 1993: Physics of Medical Imaging, vol. 1896 of Proceedings of SPIE, pp. 341–352, February 1993.
- Y. Wu, K. Doi, M. L. Giger, and R. M. Nishikawa, “Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks,” Medical Physics, vol. 19, no. 3, pp. 555–560, 1992.
- H. P. Chan, S. C. B. Lo, B. Sahiner, K. L. Lam, and M. A. Helvie, “Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network,” Medical Physics, vol. 22, no. 10, pp. 1555–1567, 1995.
- R. H. Nagel, R. M. Nishikawa, J. Papaioannou, and K. Doi, “Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms,” Medical Physics, vol. 25, no. 8, pp. 1502–1506, 1998.
- A. Papadopoulos, D. I. Fotiadis, and A. Likas, “An automatic microcalcification detection system based on a hybrid neural network classifier,” Artificial Intelligence in Medicine, vol. 25, no. 2, pp. 149–167, 2002.
- G. Rezai-Rad and S. Jamarani, “Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network,” in Proceedings of the International Conference on Computer Graphics, Imaging and Vision: New Trends, pp. 197–201, July 2005.
- L. Zhang, W. Qian, R. Sankar, D. Song, and R. Clark, “A new false positive reduction method for MCCs detection in digital mammography,” in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 1033–1036, Salt Lake City, Utah, USA, May 2001.
- Y. Wu, M. L. Giger, K. Doi, C. J. Vyborny, R. A. Schmidt, and C. E. Metz, “Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer,” Radiology, vol. 187, no. 1, pp. 81–87, 1993.
- J. A. Baker, P. J. Kornguth, J. Y. Lo, M. E. Williford, and C. E. Floyd, “Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon,” Radiology, vol. 196, no. 3, pp. 817–822, 1995.
- J. Y. Lo, J. A. Baker, P. J. Kornguth, and C. E. Floyd, “Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks,” Academic Radiology, vol. 6, no. 1, pp. 10–15, 1999.
- T. Ayer, O. Alagoz, J. Chhatwal, J. W. Shavlik, C. E. Kahn, and E. S. Burnside, “Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration,” Cancer, vol. 116, no. 14, pp. 3310–3321, 2010.
- H. P. Chan, B. Sahiner, K. L. Lam et al., “Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces,” Medical Physics, vol. 25, no. 10, pp. 2007–2019, 1998.
- Y. Jiang, R. M. Nishikawa, D. E. Wolverton et al., “Malignant and benign clustered microcalcifications: automated feature analysis and classification,” Radiology, vol. 198, no. 3, pp. 671–678, 1996.
- Y. Jiang, R. M. Nishikawa, R. A. Schmidt, C. E. Metz, M. L. Giger, and K. Doi, “Improving breast cancer diagnosis with computer-aided diagnosis,” Academic Radiology, vol. 6, no. 1, pp. 22–33, 1999.
- Z. Huo, M. L. Giger, C. J. Vyborny, D. E. Wolverton, R. A. Schmidt, and K. Doi, “Automated computerized classification of malignant and benign masses on digitized mammograms,” Academic Radiology, vol. 5, no. 3, pp. 155–168, 1998.
- H. P. Chan, B. Sahiner, N. Patrick et al., “Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network,” Physics in Medicine and Biology, vol. 42, no. 3, pp. 549–567, 1997.
- J. L. Jesneck, J. Y. Lo, and J. A. Baker, “Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors,” Radiology, vol. 244, no. 2, pp. 390–398, 2007.
- G. D. Tourassi, M. K. Markey, J. Y. Lo, and C. E. Floyd, “A neural network approach to breast cancer diagnosis as a constraint satisfaction problem,” Medical Physics, vol. 28, no. 5, pp. 804–811, 2001.
- G. D. Tourassi, J. Y. Lo, and M. K. Markey, “Validation of a constraint satisfaction neural network for breast cancer diagnosis: new results from 1,030 cases,” in Medical Imaging 2003: Image Processing, vol. 5032 of Proceedings of SPIE, pp. 207–214, February 2003.
- C. A. Beam, E. F. Conant, and E. A. Sickles, “Association of volume-independent factors with accuracy in screening mammogram interpretation,” Journal of the National Cancer Institute, vol. 95, no. 4, pp. 282–290, 2003.
- Y. Jiang, R. M. Nishikawa, R. A. Schmidt, A. Y. Toledano, and K. Doi, “Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications,” Radiology, vol. 220, no. 3, pp. 787–794, 2001.
- R. K. Orr, “Use of an artificial neural network to quantitate risk of malignancy for abnormal mammograms,” Surgery, vol. 129, no. 4, pp. 459–466, 2001.
- N. R. Cook, “Use and misuse of the receiver operating characteristic curve in risk prediction,” Circulation, vol. 115, no. 7, pp. 928–935, 2007.
- N. R. Cook, “Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve,” Clinical Chemistry, vol. 54, no. 1, pp. 17–23, 2008.
- G. A. Diamond, “What price perfection? Calibration and discrimination of clinical prediction models,” Journal of Clinical Epidemiology, vol. 45, no. 1, pp. 85–89, 1992.
- M. H. Gail and R. M. Pfeiffer, “On criteria for evaluating models of absolute risk,” Biostatistics, vol. 6, no. 2, pp. 227–239, 2005.
- P. W. F. Wilson, R. B. D'Agostino, D. Levy, A. M. Belanger, H. Silbershatz, and W. B. Kannel, “Prediction of coronary heart disease using risk factor categories,” Circulation, vol. 97, no. 18, pp. 1837–1847, 1998.
- M. A. Mazurowski, P. A. Habas, J. M. Zurada, and G. D. Tourassi, “Impact of low class prevalence on the performance evaluation of neural network based classifiers: experimental study in the context of computer-assisted medical diagnosis,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '07), pp. 2005–2009, Orlando, Fla, USA, August 2007.
- M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, and G. D. Tourassi, “Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance,” Neural Networks, vol. 21, no. 2-3, pp. 427–436, 2008.
- S. Dreiseitl and L. Ohno-Machado, “Logistic regression and artificial neural network classification models: a methodology review,” Journal of Biomedical Informatics, vol. 35, no. 5-6, pp. 352–359, 2002.
- J. V. Tu, “Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes,” Journal of Clinical Epidemiology, vol. 49, no. 11, pp. 1225–1231, 1996.
- G. Schwarzer, W. Vach, and M. Schumacher, “On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology,” Statistics in Medicine, vol. 19, no. 4, pp. 541–561, 2000.