Prediction of Near-term Breast Cancer Risk Based on Bilateral Mammographic Feature Asymmetry

Academic Radiology

Volumn 20, Issue 12, 2013, Pages 1542-1550

  Tan, Maxine ^a   Zheng, Bin ^a   Ramalingam, Pandiyarajan ^b   Gur, David ^b  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

Bilateral mammographic feature asymmetry; Breast cancer; Cancer risk prediction model; Computer aided detection (CAD) of mammograms

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; BREAST CANCER; CANCER RISK; CANCER SCREENING; FEASIBILITY STUDY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MAMMOGRAPHY; PREDICTION; PRIORITY JOURNAL; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SUPPORT VECTOR MACHINE;

BILATERAL MAMMOGRAPHIC FEATURE ASYMMETRY; BREAST CANCER; CANCER RISK PREDICTION MODEL; COMPUTER-AIDED DETECTION (CAD) OF MAMMOGRAMS;

BREAST NEOPLASMS; FEASIBILITY STUDIES; FEMALE; FOLLOW-UP STUDIES; HUMANS; MAMMOGRAPHY; ODDS RATIO; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RETROSPECTIVE STUDIES; RISK ASSESSMENT; ROC CURVE; SUPPORT VECTOR MACHINES;

EID: 84887210395     PISSN: 10766332     EISSN: 18784046     Source Type: Journal    
DOI: 10.1016/j.acra.2013.08.020     Document Type: Article

References (57)

1. Siegel R., Naishadham D., Jemal A. Cancer statistics, 2013. CA Cancer JClin 2013, 63(1):11-30.
2. Madigan M.P., Ziegler R.G., Benichou J., et al. Proportion of breast cancer cases in the United States explained by well-established risk factors. JNatl Cancer Inst 1995, 87(22):1681-1685.
3. Amir E., Freedman O.C., Seruga B., et al. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst 2010, 102(10):680-691.
4. Tabar L., Vitak B., Chen H.H., et al. Beyond randomized controlled trials: organized mammographic screening substantially reduces breast carcinoma mortality. Cancer 2001, 91(9):1724-1731.
5. Smith R.A., Cokkinides V., Brooks D., et al. Cancer screening in the United States, 2011: A review of current American Cancer Society guidelines and issues in cancer screening. CA Cancer J Clin 2011, 61(1):8-30.
6. Sickles E.A., Wolverton D.E., Dee K.E. Performance parameters for screening and diagnostic mammography: specialist and general radiologists. Radiology 2002, 224(3):861-869.
7. Kopans D.B. The positive predictive value of mammography. AJR Am J Rotentgenol 1991, 158:521-526.
8. Fenton J.J., Wheeler J., Carney P.A., et al. Reality check: perceived versus actual performance of community mammographers. AJR Am J Rotentgenol 2006, 187:42-46.
9. Hubbard R.A., Kerlikowske K., Flowers C.I., et al. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: a cohort study. Ann Intern Med 2011, 155:481-492.
10. Brodersen J., Siersma V.D. Long-term psychosocial consequences of false-positive screening mammography. Ann Fam Med 2013, 11(2):106-115.
11. Yaffe M.J., Mainprize J.G. Risk of radiation-induced breast cancer from mammographic screening. Radiology 2011, 258(1):98-105.
12. Buist D.S., Anderson M.L., Haneuse S.J., et al. Influence of annual interpretive volume on screening mammography performance in the United States. Radiology 2011, 259(1):72-84.
13. Nelson H.D., Tyne K., Naik A., et al. Screening for breast cancer: an update for the U.S. Preventive Services Task Force. Ann Intern Med 2009, 151(10):727-737.
14. Berlin L., Hall F.M. More mammography muddle: emotions, politics, science, costs, and polarization. Radiology 2010, 255(2):311-316.
15. Brawley O.W. Risk-based mammography screening: an effort to maximize the benefits and minimize the harms. Ann Intern Med 2012, 156(9):662-663.
16. Tice J.A., Cummings S.R., Smith-Bindman R., et al. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 2008, 148(5):337-347.
17. Schousboe J.T., Kerlikowske K., Loh A., et al. Personalizing mammographyby breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med 2011, 155(1):10-20.
18. van Ravesteyn N.T., Miglioretti D.L., Stout N.K., et al. Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk. Ann Intern Med 2012, 156:609-617.
19. Nelson H.D., Zakher B., Cantor A., et al. Risk factors for breast cancer for women aged 40 to 49 years: a systematic review and meta-analysis. Ann Intern Med 2012, 156(9):635-648.
20. Saftlas A.F., Hoover R.N., Brinton L.A., et al. Mammographic densities and risk of breast cancer. Cancer 1991, 67(11):2833-2838.
21. Boyd N.F., Martin I.J., Stone J., et al. Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention. Curr Oncol Rep 2001, 3(5):314-321.
22. Bi-RADS Breast Imaging Reporting andData System breast imaging atlas 2003, American College of Radiology, American College of Radiology, Reston, VA.
23. Berg W.A., Campassi C., Langenberg P., et al. Breast Imaging Reporting andData System: inter- and intraobserver variability in feature analysis and final assessment. AJR Am J Roentgenol 2000, 174(6):1769-1777.
24. Taboces P.G., Correa J., Souto M., et al. Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. Phys Med Biol 1995, 40(1):103-117.
25. Byng J.W., Yaffe M.J., Lockwood G.A., et al. Automated analysis of mammographic densities and breast carcinoma risk. Cancer 1997, 80(1):66-74.
26. Heine J.J., Velthuizen R.P. Spectral analysis of full field digital mammography data. Med Phys 2002, 29(5):647-661.
27. Zhou C., Chan H.P., Petrick N., et al. Computerized image analysis: estimation of breast density on mammograms. Med Phys 2001, 28(6):1056-1069.
28. Chang Y.-H., Wang X.-H., Hardesty L.A., et al. Computerized assessment of tissue composition on digitized mammograms. Acad Radiol 2002, 9(8):899-905.
29. Pawluczyk O., Augustine B.J., Yaffe M.J., et al. A volumetric method for estimation of breast density on digitized screen-film mammograms. Med Phys 2003, 30(3):352-364.
30. Wang X.H., Good W.F., Chapman B.E., et al. Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography. AJR Am J Roentgenol 2003, 180(1):257-262.
31. Wei J., Chan H.-P., Helvie M.A., et al. Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. Med Phys 2004, 31(4):933-942.
32. Li H., Giger M.L., Olopade O.I., et al. Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad Radiol 2005, 12(7):863-873.
33. Glide-Hurst C.K., Duric N., Littrup P. A new method for quantitative analysis of mammographic density. Med Phys 2007, 34(11):4491-4498.
34. Wei J., Chan H.P., Wu Y.T., et al. Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study. Radiology 2011, 260(1):42-49.
35. Gail M.H., Mai P.L. Comparing breast cancer risk assessment models. J Natl Cancer Inst 2010, 102(10):665-668.
36. Harvey J.A., Bovbjerg V.E. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 2004, 230(1):29-41.
37. Kopans D.B. Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology 2008, 246(2):348-353.
38. van Gils C.H., Otten J.D., Verbeek A.L., et al. Mammographic breast density and risk of breast cancer: masking bias or causality?. Eur J Epidemiol 1998, 14(4):315-320.
39. Colin C., Prince V., Alette P.J. Can mammographic assessment lead to consider density as a risk factor for breast cancer?. Eur J Radiol 2013, 82(3):404-411.
40. Passaperuma K., Warner E., Hill K.A., et al. Is mammographic breast density a breast cancer risk factor in women with BRCA mutations?. J Clin Oncol 2010, 28(23):3779-3783.
41. Wang X., Lederman D., Tan J., et al. Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification. Acad Radiol 2010, 17(10):1234-1241.
42. Wang X., Lederman D., Tan J., et al. Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry. Med Eng Phys 2011, 33(8):934-942.
43. Zheng B., Sumkin J.H., Zuley M.L., et al. Bilateral mammographic density asymmetry and breast cancer risk: a preliminary assessment. Eur J Radiol 2012, 81(11):3222-3228.
44. Zheng B., Leader J.K., Abrams G.S., et al. Multiview-based computer-aided detection scheme for breast masses. Med Phys 2006, 33(9):3135-3143.
45. Tang X. Texture information in run-length matrices. IEEE Trans Image Process 1998, 7(11):1602-1609.
46. Tzikopoulos S., Mavroforakis M.E., Georgiou H.V., et al. A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Methods Programs Biomed 2011, 102(1):47-63.
47. Powell W.B. Approximate dynamic programming: solving the curses of dimensionality 2007, Wiley-Interscience, New Jersey. 1st edition.
48. Xu L., Yan P., Chang T. Best first strategy for feature selection. 9th International Conference on Pattern Recognition 1988, 706-708. IEEE Computer Society Press, Rome, Italy, Rockville, MD.
49. Caruana R., Freitag D. Greedy attribute selection. 11th International Conference on Machine Learning 1994, 28-36. Morgan Kaufmann, New Brunswick, NJ, San Mateo, CA. W.W. Cohen, H. Hirsh (Eds.).
50. Pudil P., Novovicova J., Kittler J. Floating search methods in feature selection. Pattern Recogn Lett 1994, 15(11):1119-1125.
51. Theodoridis S., Koutroumbas K. Pattern recognition 2008, Academic Press, San Diego, CA, USA. 4th edition.
52. Theodoridis S., Koutroumbas K. Introduction to pattern recognition: a MATLAB approach 2010, Academic Press, Burlington, MA, USA.
53. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011, 2(3):27.
54. Hsu C.-W., Chang C.-C., Lin C.-J. A practical guide to support vector classification. Technical report 2009, National Taiwan University, Taipei, Taiwan.
55. Steyerberg E.W., Vickers A.J., Cook N.R., et al. Assessing the performance ofprediction models: a framework for traditional and novel measures. Epidemiology 2010, 21(1):128-138.
56. The California report. California's breast density notification law goes into effect. Available at: Accessed: July 23, 2013.2013. http://blogs.kqed.org/stateofhealth/2013/03/29/californias-breast-density-notification-law-goes-into-effect/.
57. Scutt D., Manning J.T., Whitehouse G.H., et al. The relationship between breast asymmetry, breast size and the occurrence of breast cancer. Br J Radiol 1997, 70(838):1017-1021.