Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

References

• Alagoz O., Maillart L. M., Schaefer A. J., Roberts M. S. The optimal timing of living-donor liver transplantation. Management Sci. (2004) 50(10):1420–1430Link, Google Scholar
• American Cancer Society Breast cancer facts and figures: 2010. (2010) . American Cancer Society, AtlantaGoogle Scholar
• Arias E. United States life tables, 2003. National Vital. Stat. Rep. (2006) 54(14):1–40Google Scholar
• Ayer T., Alagoz O., Chhatwal O., Shavlik J. W., Kahn C. E., Burnside E. S. Breast cancer risk estimation with artificial neural networks revisited: Discrimination and calibration. Cancer (2010) 116(14):3310–3321Crossref, Google Scholar
• Baines C., Dayan J. R. A tangled web: Factors likely to affect the efficacy of screening mammography. J. National Cancer Inst. (1999) 91(10):833–838Crossref, Google Scholar
• Baker J. A., Kornguth P. J., Lo J. Y., Williford M. E., Floyd C. E. Breast cancer: Prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology (1995) 196(3):817–822Crossref, Google Scholar
• Barlow R. E., Proschan F.Mathematical Theory of Reliability (1965) (Wiley, New York) Google Scholar
• Barlow W. E., Chi C., Carney P. A., Taplin S. H., D'Orsi C., Cutter G., Hendrick R. E., Elmore J. G. Accuracy of screening mammography interpretation by characteristics of radiologists. J. National Cancer Inst. (2004) 96(24):1840–1850Crossref, Google Scholar
• Beam C. A., Layde P. M., Sullivan D. C. Variability in the interpretation of screening mammograms by U.S. radiologists. Findings from a national sample. Arch. Intern. Med. (1996) 156(2):209–213Crossref, Google Scholar
• BI-RADSBreast Imaging Reporting and Data System (BI-RADS) (1998) 3rd ed.(American College of Radiology, Reston, VA) Google Scholar
• Bird R. E., Wallace T. W., Yankaskas B. C. Analysis of cancers missed at screening mammography. Radiology (1992) 184(3):613–617Crossref, Google Scholar
• Burnside E. S., Rubin D. L., Fine J. P., Shachter R. D., Sisney G. A., Leung W. K. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: Initial experience. Radiology (2006) 240(3):666–673Crossref, Google Scholar
• Burnside E. S., Davis J., Chhatwal J., Alagoz O., Lindstrom M. J., Geller B. M., Littenberg B., Shaffer K. A., Kahn C. E., Page C. D. A probabilistic computer model developed from clinical data in the national mammography database format to classify mammography findings. Radiology (2009) 251(3):663–672Crossref, Google Scholar
• Chan E. C. Promoting an ethical approach to unproven imaging tests. J. Amer. Coll. Radiol. (2005) 2(4):311–320Crossref, Google Scholar
• Chhatwal J., Alagoz O., Lindstrom M. J., Shaffer K. A., Kahn C. E., Burnside E. S. A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. Amer. J. Roentgenol. (2009) 192(4):1117–1127Crossref, Google Scholar
• Destounis S. V., DiNitto P., Logan-Young W., Bonaccio E., Zuley M. L., Willison K. M. Can computer-aided detection with double reading of screening mammograms help decrease the false-negative rate? Initial experience. Radiology (2004) 232(2):578–584Crossref, Google Scholar
• Drummond M. F.Methods for the Economic Evaluation of Health Care Programmes (2005) (Oxford University Press, Oxford, UK) Google Scholar
• Elvecrog E. L. Nonpalpable breast lesions: Correlation of stereotaxic large-core needle biopsy and surgical biopsy results. Radiology (1993) 188(2):453–455Crossref, Google Scholar
• Ernster V. L., Barclay J., Kerlikowske K., Grady D., Henderson C. Incidence of and treatment for ductal carcinoma in situ of the breast. JAMA (1996) 275(12):913–918Crossref, Google Scholar
• Ernster V. L., Ballard-Barbash R., Barlow W. E., Zheng Y., Weaver D. L., Cutter G., Yankaskas B. C., et al. Detection of ductal carcinoma in situ in women undergoing screening mammography. J. National Cancer Inst. (2002) 94(20):1546–1554Crossref, Google Scholar
• Fan K. Subadditive functions on a distributive lattice and an extension of Szász's. J. Math. Anal. Appl. (1967) 18:262–268Crossref, Google Scholar
• Fowble B. L., Schultz D. J., Overmoyer B., Solin L. J., Fox K., Jardines L., Orel S., Glick J. H. The influence of young age on outcome in early stage breast cancer. Internat. J. Radiat. Oncol. Biol. Phys. (1994) 30(1):23–33Crossref, Google Scholar
• Fracheboud J., Groenewoud J. H., Boer R., Draisma G., de Bruijn A. E., Verbeek A. L., de Koning H. J. Seventy-five years is an appropriate upper age limit for population-based mammography screening. Internat. J. Cancer (2005) 118(8):2020–2025Crossref, Google Scholar
• Freid V. M., Prager K., MacKay A. P., Xia H. Chartbook on trends in the health of Americans. Health, United States, 2003 (2003) (National Center for Health Statistics, Hyattsville, MD) Google Scholar
• Fryback D. G., Stout N. K., Rosenberg M. A., Trentham-Dietz A., Kuruchittham V., Remington P. L. Chapter 7: The Wisconsin breast cancer epidemiology simulation model. J. National Cancer Inst. Mono. (2006) 2006(36):37–47Crossref, Google Scholar
• Haybittle J. L. Life expectancy as a measurement of the benefit shown by clinical trials of treatment for early breast cancer. Clin. Oncol. (R. Coll. Radiol.) (1998) 10(2):92–94Crossref, Google Scholar
• Hillman B. J. Informed and shared decision making: An alternative to the debate over unproven screening tests. J. Amer. College Radiology (2005) 2(4):297–298Crossref, Google Scholar
• Ivy J. S. Balancing patient and payer preferences: A maintenance-based model for breast cancer treatment and detection. (2006) . Working paper, North Carolina State University, RaleighGoogle Scholar
• Jayasinghe U. W., Taylor R., Boyages J. Is age at diagnosis an independent prognostic factor for survival following breast cancer? ANZ J. Surgery (2005) 75(9):762–767Crossref, Google Scholar
• Jemal A., Siegel R., Ward E., Murray T., Xu J., Thun M. J. Cancer statistics, 2007. CA: A Cancer J. Clinicians (2007) 57(1):43–66Crossref, Google Scholar
• Jesneck J. L., Nolte L. W., Baker J. A., Floyd C. E., Lo J. Y. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Medical Phys. (2006) 33(8):2945–2954Crossref, Google Scholar
• Jiang Y., Nishikawa R. M., Schmidt R. A., Toledano A. Y., Doi K. Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcification. Radiology (2001) 220(3):787–794Crossref, Google Scholar
• Jiang Y., Nishikawa R. M., Schmidt R. A., Metz C. E., Giger M. L., Doi K. Improving breast cancer diagnosis with computer assisted diagnosis. Acad. Radiol. (1999) 6(1):22–33Crossref, Google Scholar
• Kerlikowske K., Grady D., Rubin S. M., Sandrock C., Ernster V. L. Efficacy of screening mammography. A meta-analysis. JAMA (1995) 273(2):149–154Crossref, Google Scholar
• Lee C. H., Philpotts L. E., Horvath L. J., Tocino I. Follow-up of breast lesions diagnosed as benign with stereotactic core-needle biopsy: Frequency of mammographic change and false-negative rate. Radiology (1999) 212(1):189–194Crossref, Google Scholar
• Liberman L. Centennial dissertation. Percutaneous imaging-guided core breast biopsy: State of the art at the millennium. Amer. J. Roentgenol. (2000) 174(5):1191–1199Crossref, Google Scholar
• Liberman L., Menell J. H. Breast imaging reporting and data system (BI-RADS). Radiol. Clin. North Amer. (2002) 40(3):409–430Crossref, Google Scholar
• Maillart L., Ivy J. S., Ransom S., Diehl K. Assessing dynamic breast cancer screening policies. Oper. Res. (2008) 56(6):1411–1427Link, Google Scholar
• Maxwell J. R., Bugbee M. E., Wellisch D., Shalmon A., Sayre J., Bassett L. W. Imaging-guided core needle biopsy of the breast: Study of psychological outcomes. Breast J. (2000) 6(1):53–61Crossref, Google Scholar
• Meyer J. E., Eberlein T. J., Stomper P. C., Sonnenfeld M. R. Biopsy of occult breast lesions. Analysis of 1261 abnormalities. JAMA (1990) 263(17):2341–2343Crossref, Google Scholar
• Michaelson J. S., Halpern E., Kopans D. B. Breast cancer: Computer simulation method for estimating optimal intervals for screening. Radiology (1999) 212(2):551–560Crossref, Google Scholar
• Murphy A. M. Mammography screening for breast cancer: A view from 2 worlds. JAMA (2010) 303(2):166–167Crossref, Google Scholar
• Nelson H. D., Tyne K., Naik A., Bougatsos C., Chan B. K., Humphrey L. Screening for breast cancer: An update for the U.S. preventive services task force. Ann. Internal Medicine (2009) 151(10):727–737Crossref, Google Scholar
• Nyström L., Andersson I., Bjurstam N., Frisell J., Nordenskjöld B., Rutqvist L. E. Long-term effects of mammography screening: Updated overview of the Swedish randomised trials. Lancet (2002) 359(9310):909–919Crossref, Google Scholar
• Opie H., Estes N. C., Jewell W. R., Chang C. H., Thomas J. A., Estes M. A. Breast biopsy for nonpalpable lesions: A worthwhile endeavor? Amer. Surg. (1993) 59(8):490–493Google Scholar
• Parker S. H. Nonpalpable breast lesions: Stereotactic automated large-core biopsies. Radiology (1991) 180(2):403–407Crossref, Google Scholar
• Partridge A. H., Winer E. P. On mammography—More agreement than disagreement. New England J. Medicine (2009) 361(26):2499–2501Crossref, Google Scholar
• Pliskin J. S., Shepard D. S., Weinstein M. C. Utility functions for life years and health status. Oper. Res. (1980) 28(1):206–224Link, Google Scholar
• Puterman M. L.Markov Decision Processes: Discrete Stochastic Dynamic Programming (1994) (John Wiley & Sons, Inc., New York) Crossref, Google Scholar
• Schairer C., Mink P. J., Carroll L., Devesa S. S. Probabilities of death from breast cancer and other causes among female breast cancer patients. J. National Cancer Inst. (2004) 96(17):1311–1321Crossref, Google Scholar
• Sickles E. A., Wolverton D. E., Dee K. E. Performance parameters for screening and diagnostic mammography: Specialist and general radiologists. Radiology (2002) 224(3):861–869Crossref, Google Scholar
• Smith R. A., Cokkinides V., Eyre H. J. American Cancer Society guidelines for the early detection of cancer. CA: A Cancer J. Clinicians (2006) 56(1):11–25Crossref, Google Scholar
• Smith R. A., Saslow D., Andrews Sawyer K., Burke W., Costanza M. E., Evans W. P., Foster R. S., Hendrick E., Eyre H. J., Sener S. American Cancer Society guidelines for breast cancer screening: Update 2003. CA: A Cancer J. Clinicians (2003) 53(3):141–169Crossref, Google Scholar
• Smith-Bindman R., Chu P. W., Miglioretti D. L., Sickles E. A., Blanks R., Ballard-Barbash R., Bobo J. K., et al. Comparison of screening mammography in the United States and the United Kingdom. JAMA (2003) 290(16):2129–2137Crossref, Google Scholar
• Sonnenberg F. A., Beck J. R. Markov models in medical decision making: A practical guide. Medical Decision Making (1993) 13(4):322–338Crossref, Google Scholar
• Steggles S., Lightfoot N., Sellick S. M. Psychological distress associated with organized breast cancer screening. Cancer Prev. Control (1998) 2(5):213–220Google Scholar
• Stone M. Cross-validation choice and assessment of statistical procedures. J. Roy. Statist. Soc. (1974) 36(2):111–147Google Scholar
• Tversky A., Kahneman D., Shafir E. Judgment under uncertainty: Heuristics and biases. Preference, Belief, and Similarity: Selected Writings. Amos Tversky (2004) (MIT Press, Cambridge, MA) Crossref, Google Scholar
• Velanovich V. Immediate biopsy versus observation for abnormal findings on mammograms: An analysis of potential outcomes and costs. Amer. J. Surg. (1995) 170(4):327–332Crossref, Google Scholar
• Williams R. S., Willard H. F., Snyderman R. Personalized health planning. Science (2003) 300(5619):549Crossref, Google Scholar